diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 56d02e5bf..3e4ef2faa 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -80,6 +80,11 @@ jobs: # Allow failure for coveralls coveralls || true + - name: Run json schema extract + # This should be kept before the repository check to ensure that the schema is up-to-date + run: | + python build_helpers/extract_config_json_schema.py + - name: Check for repository changes run: | if [ -n "$(git status --porcelain)" ]; then diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8fa4bf9ee..c78811f30 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,14 +9,14 @@ repos: # stages: [push] - repo: https://github.com/pre-commit/mirrors-mypy - rev: "v1.10.1" + rev: "v1.11.0" hooks: - id: mypy exclude: build_helpers additional_dependencies: - - types-cachetools==5.3.0.7 + - types-cachetools==5.4.0.20240717 - types-filelock==3.2.7 - - types-requests==2.32.0.20240622 + - types-requests==2.32.0.20240712 - types-tabulate==0.9.0.20240106 - types-python-dateutil==2.9.0.20240316 - SQLAlchemy==2.0.31 @@ -31,7 +31,7 @@ repos: - repo: https://github.com/charliermarsh/ruff-pre-commit # Ruff version. - rev: 'v0.5.0' + rev: 'v0.5.4' hooks: - id: ruff diff --git a/build_helpers/extract_config_json_schema.py b/build_helpers/extract_config_json_schema.py new file mode 100644 index 000000000..44ecc0f91 --- /dev/null +++ b/build_helpers/extract_config_json_schema.py @@ -0,0 +1,17 @@ +"""Script to extract the configuration json schema from config_schema.py file.""" + +from pathlib import Path + +import rapidjson + +from freqtrade.configuration.config_schema import CONF_SCHEMA + + +def extract_config_json_schema(): + schema_filename = Path(__file__).parent / "schema.json" + with schema_filename.open("w") as f: + rapidjson.dump(CONF_SCHEMA, f, indent=2) + + +if __name__ == "__main__": + extract_config_json_schema() diff --git a/build_helpers/pyarrow-16.1.0-cp39-cp39-linux_armv7l.whl b/build_helpers/pyarrow-16.1.0-cp39-cp39-linux_armv7l.whl deleted file mode 100644 index ffbe09bf4..000000000 Binary files a/build_helpers/pyarrow-16.1.0-cp39-cp39-linux_armv7l.whl and /dev/null differ diff --git a/build_helpers/pyarrow-16.1.0-cp311-cp311-linux_armv7l.whl b/build_helpers/pyarrow-17.0.0-cp311-cp311-linux_armv7l.whl similarity index 62% rename from build_helpers/pyarrow-16.1.0-cp311-cp311-linux_armv7l.whl rename to build_helpers/pyarrow-17.0.0-cp311-cp311-linux_armv7l.whl index 345f2dd63..4fe505588 100644 Binary files a/build_helpers/pyarrow-16.1.0-cp311-cp311-linux_armv7l.whl and b/build_helpers/pyarrow-17.0.0-cp311-cp311-linux_armv7l.whl differ diff --git a/build_helpers/schema.json b/build_helpers/schema.json new file mode 100644 index 000000000..c0933d9f8 --- /dev/null +++ b/build_helpers/schema.json @@ -0,0 +1,1601 @@ +{ + "type": "object", + "properties": { + "max_open_trades": { + "description": "Maximum number of open trades. -1 for unlimited.", + "type": [ + "integer", + "number" + ], + "minimum": -1 + }, + "new_pairs_days": { + "description": "Download data of new pairs for given number of days", + "type": "integer", + "default": 30 + }, + "timeframe": { + "description": "The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). \nUsually specified in the strategy and missing in the configuration.", + "type": "string" + }, + "stake_currency": { + "description": "Currency used for staking.", + "type": "string" + }, + "stake_amount": { + "description": "Amount to stake per trade.", + "type": [ + "number", + "string" + ], + "minimum": 0.0001, + "pattern": "unlimited" + }, + "tradable_balance_ratio": { + "description": "Ratio of balance that is tradable.", + "type": "number", + "minimum": 0.0, + "maximum": 1, + "default": 0.99 + }, + "available_capital": { + "description": "Total capital available for trading.", + "type": "number", + "minimum": 0 + }, + "amend_last_stake_amount": { + "description": "Whether to amend the last stake amount.", + "type": "boolean", + "default": false + }, + "last_stake_amount_min_ratio": { + "description": "Minimum ratio for the last stake amount.", + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.5 + }, + "fiat_display_currency": { + "description": "Fiat currency for display purposes.", + "type": "string", + "enum": [ + "AUD", + "BRL", + "CAD", + "CHF", + "CLP", + "CNY", + "CZK", + "DKK", + "EUR", + "GBP", + "HKD", + "HUF", + "IDR", + "ILS", + "INR", + "JPY", + "KRW", + "MXN", + "MYR", + "NOK", + "NZD", + "PHP", + "PKR", + "PLN", + "RUB", + "UAH", + "SEK", + "SGD", + "THB", + "TRY", + "TWD", + "ZAR", + "USD", + "BTC", + "ETH", + "XRP", + "LTC", + "BCH", + "BNB", + "" + ] + }, + "dry_run": { + "description": "Enable or disable dry run mode.", + "type": "boolean" + }, + "dry_run_wallet": { + "description": "Initial wallet balance for dry run mode.", + "type": "number", + "default": 1000 + }, + "cancel_open_orders_on_exit": { + "description": "Cancel open orders when exiting.", + "type": "boolean", + "default": false + }, + "process_only_new_candles": { + "description": "Process only new candles.", + "type": "boolean" + }, + "minimal_roi": { + "description": "Minimum return on investment. \nUsually specified in the strategy and missing in the configuration.", + "type": "object", + "patternProperties": { + "^[0-9.]+$": { + "type": "number" + } + } + }, + "amount_reserve_percent": { + "description": "Percentage of amount to reserve.", + "type": "number", + "minimum": 0.0, + "maximum": 0.5 + }, + "stoploss": { + "description": "Value (as ratio) to use as Stoploss value. \nUsually specified in the strategy and missing in the configuration.", + "type": "number", + "maximum": 0, + "exclusiveMaximum": true + }, + "trailing_stop": { + "description": "Enable or disable trailing stop. \nUsually specified in the strategy and missing in the configuration.", + "type": "boolean" + }, + "trailing_stop_positive": { + "description": "Positive offset for trailing stop. \nUsually specified in the strategy and missing in the configuration.", + "type": "number", + "minimum": 0, + "maximum": 1 + }, + "trailing_stop_positive_offset": { + "description": "Offset for trailing stop to activate. \nUsually specified in the strategy and missing in the configuration.", + "type": "number", + "minimum": 0, + "maximum": 1 + }, + "trailing_only_offset_is_reached": { + "description": "Use trailing stop only when offset is reached. \nUsually specified in the strategy and missing in the configuration.", + "type": "boolean" + }, + "use_exit_signal": { + "description": "Use exit signal for trades. \nUsually specified in the strategy and missing in the configuration.", + "type": "boolean" + }, + "exit_profit_only": { + "description": "Exit only when in profit. Exit signals are ignored as long as profit is < exit_profit_offset. \nUsually specified in the strategy and missing in the configuration.", + "type": "boolean" + }, + "exit_profit_offset": { + "description": "Offset for profit exit. \nUsually specified in the strategy and missing in the configuration.", + "type": "number" + }, + "fee": { + "description": "Trading fee percentage. Can help to simulate slippage in backtesting", + "type": "number", + "minimum": 0, + "maximum": 0.1 + }, + "ignore_roi_if_entry_signal": { + "description": "Ignore ROI if entry signal is present. \nUsually specified in the strategy and missing in the configuration.", + "type": "boolean" + }, + "ignore_buying_expired_candle_after": { + "description": "Ignore buying after candle expiration time. \nUsually specified in the strategy and missing in the configuration.", + "type": "number" + }, + "trading_mode": { + "description": "Mode of trading (e.g., spot, margin).", + "type": "string", + "enum": [ + "spot", + "margin", + "futures" + ] + }, + "margin_mode": { + "description": "Margin mode for trading.", + "type": "string", + "enum": [ + "cross", + "isolated", + "" + ] + }, + "reduce_df_footprint": { + "description": "Reduce DataFrame footprint by casting columns to float32/int32.", + "type": "boolean", + "default": false + }, + "minimum_trade_amount": { + "description": "Minimum amount for a trade - only used for lookahead-analysis", + "type": "number", + "default": 10 + }, + "targeted_trade_amount": { + "description": "Targeted trade amount for lookahead analysis.", + "type": "number", + "default": 20 + }, + "lookahead_analysis_exportfilename": { + "description": "csv Filename for lookahead analysis export.", + "type": "string" + }, + "startup_candle": { + "description": "Startup candle configuration.", + "type": "array", + "uniqueItems": true, + "default": [ + 199, + 399, + 499, + 999, + 1999 + ] + }, + "liquidation_buffer": { + "description": "Buffer ratio for liquidation.", + "type": "number", + "minimum": 0.0, + "maximum": 0.99 + }, + "backtest_breakdown": { + "description": "Breakdown configuration for backtesting.", + "type": "array", + "items": { + "type": "string", + "enum": [ + "day", + "week", + "month" + ] + } + }, + "bot_name": { + "description": "Name of the trading bot. Passed via API to a client.", + "type": "string" + }, + "unfilledtimeout": { + "description": "Timeout configuration for unfilled orders. \nUsually specified in the strategy and missing in the configuration.", + "type": "object", + "properties": { + "entry": { + "description": "Timeout for entry orders in unit.", + "type": "number", + "minimum": 1 + }, + "exit": { + "description": "Timeout for exit orders in unit.", + "type": "number", + "minimum": 1 + }, + "exit_timeout_count": { + "description": "Number of times to retry exit orders before giving up.", + "type": "number", + "minimum": 0, + "default": 0 + }, + "unit": { + "description": "Unit of time for the timeout (e.g., seconds, minutes).", + "type": "string", + "enum": [ + "minutes", + "seconds" + ], + "default": "minutes" + } + } + }, + "entry_pricing": { + "description": "Configuration for entry pricing.", + "type": "object", + "properties": { + "price_last_balance": { + "description": "Balance ratio for the last price.", + "type": "number", + "minimum": 0, + "maximum": 1, + "exclusiveMaximum": false + }, + "price_side": { + "description": "Side of the price to use (e.g., bid, ask, same).", + "type": "string", + "enum": [ + "ask", + "bid", + "same", + "other" + ], + "default": "same" + }, + "use_order_book": { + "description": "Whether to use the order book for pricing.", + "type": "boolean" + }, + "order_book_top": { + "description": "Top N levels of the order book to consider.", + "type": "integer", + "minimum": 1, + "maximum": 50 + }, + "check_depth_of_market": { + "description": "Configuration for checking the depth of the market.", + "type": "object", + "properties": { + "enabled": { + "description": "Enable or disable depth of market check.", + "type": "boolean" + }, + "bids_to_ask_delta": { + "description": "Delta between bids and asks to consider.", + "type": "number", + "minimum": 0 + } + } + } + }, + "required": [ + "price_side" + ] + }, + "exit_pricing": { + "description": "Configuration for exit pricing.", + "type": "object", + "properties": { + "price_side": { + "description": "Side of the price to use (e.g., bid, ask, same).", + "type": "string", + "enum": [ + "ask", + "bid", + "same", + "other" + ], + "default": "same" + }, + "price_last_balance": { + "description": "Balance ratio for the last price.", + "type": "number", + "minimum": 0, + "maximum": 1, + "exclusiveMaximum": false + }, + "use_order_book": { + "description": "Whether to use the order book for pricing.", + "type": "boolean" + }, + "order_book_top": { + "description": "Top N levels of the order book to consider.", + "type": "integer", + "minimum": 1, + "maximum": 50 + } + }, + "required": [ + "price_side" + ] + }, + "custom_price_max_distance_ratio": { + "description": "Maximum distance ratio between current and custom entry or exit price.", + "type": "number", + "minimum": 0.0, + "maximum": 1, + "default": 0.02 + }, + "order_types": { + "description": "Configuration of order types. \nUsually specified in the strategy and missing in the configuration.", + "type": "object", + "properties": { + "entry": { + "description": "Order type for entry (e.g., limit, market).", + "type": "string", + "enum": [ + "limit", + "market" + ] + }, + "exit": { + "description": "Order type for exit (e.g., limit, market).", + "type": "string", + "enum": [ + "limit", + "market" + ] + }, + "force_exit": { + "description": "Order type for forced exit (e.g., limit, market).", + "type": "string", + "enum": [ + "limit", + "market" + ] + }, + "force_entry": { + "description": "Order type for forced entry (e.g., limit, market).", + "type": "string", + "enum": [ + "limit", + "market" + ] + }, + "emergency_exit": { + "description": "Order type for emergency exit (e.g., limit, market).", + "type": "string", + "enum": [ + "limit", + "market" + ], + "default": "market" + }, + "stoploss": { + "description": "Order type for stop loss (e.g., limit, market).", + "type": "string", + "enum": [ + "limit", + "market" + ] + }, + "stoploss_on_exchange": { + "description": "Whether to place stop loss on the exchange.", + "type": "boolean" + }, + "stoploss_price_type": { + "description": "Price type for stop loss (e.g., last, mark, index).", + "type": "string", + "enum": [ + "last", + "mark", + "index" + ] + }, + "stoploss_on_exchange_interval": { + "description": "Interval for stop loss on exchange in seconds.", + "type": "number" + }, + "stoploss_on_exchange_limit_ratio": { + "description": "Limit ratio for stop loss on exchange.", + "type": "number", + "minimum": 0.0, + "maximum": 1.0 + } + }, + "required": [ + "entry", + "exit", + "stoploss", + "stoploss_on_exchange" + ] + }, + "order_time_in_force": { + "description": "Time in force configuration for orders. \nUsually specified in the strategy and missing in the configuration.", + "type": "object", + "properties": { + "entry": { + "description": "Time in force for entry orders.", + "type": "string", + "enum": [ + "GTC", + "FOK", + "IOC", + "PO", + "gtc", + "fok", + "ioc", + "po" + ] + }, + "exit": { + "description": "Time in force for exit orders.", + "type": "string", + "enum": [ + "GTC", + "FOK", + "IOC", + "PO", + "gtc", + "fok", + "ioc", + "po" + ] + } + }, + "required": [ + "entry", + "exit" + ] + }, + "coingecko": { + "description": "Configuration for CoinGecko API.", + "type": "object", + "properties": { + "is_demo": { + "description": "Whether to use CoinGecko in demo mode.", + "type": "boolean", + "default": true + }, + "api_key": { + "description": "API key for accessing CoinGecko.", + "type": "string" + } + }, + "required": [ + "is_demo", + "api_key" + ] + }, + "exchange": { + "description": "Exchange configuration.", + "$ref": "#/definitions/exchange" + }, + "edge": { + "description": "Edge configuration.", + "$ref": "#/definitions/edge" + }, + "freqai": { + "description": "FreqAI configuration.", + "$ref": "#/definitions/freqai" + }, + "external_message_consumer": { + "description": "Configuration for external message consumer.", + "$ref": "#/definitions/external_message_consumer" + }, + "experimental": { + "description": "Experimental configuration.", + "type": "object", + "properties": { + "block_bad_exchanges": { + "type": "boolean" + } + } + }, + "pairlists": { + "description": "Configuration for pairlists.", + "type": "array", + "items": { + "type": "object", + "properties": { + "method": { + "description": "Method used for generating the pairlist.", + "type": "string", + "enum": [ + "StaticPairList", + "VolumePairList", + "ProducerPairList", + "RemotePairList", + "MarketCapPairList", + "AgeFilter", + "FullTradesFilter", + "OffsetFilter", + "PerformanceFilter", + "PrecisionFilter", + "PriceFilter", + "RangeStabilityFilter", + "ShuffleFilter", + "SpreadFilter", + "VolatilityFilter" + ] + } + }, + "required": [ + "method" + ] + } + }, + "protections": { + "description": "Configuration for various protections.", + "type": "array", + "items": { + "type": "object", + "properties": { + "method": { + "description": "Method used for the protection.", + "type": "string", + "enum": [ + "CooldownPeriod", + "LowProfitPairs", + "MaxDrawdown", + "StoplossGuard" + ] + }, + "stop_duration": { + "description": "Duration to lock the pair after a protection is triggered, in minutes.", + "type": "number", + "minimum": 0.0 + }, + "stop_duration_candles": { + "description": "Duration to lock the pair after a protection is triggered, in number of candles.", + "type": "number", + "minimum": 0 + }, + "trade_limit": { + "description": "Minimum number of trades required during lookback period.", + "type": "number", + "minimum": 1 + }, + "lookback_period": { + "description": "Period to look back for protection checks, in minutes.", + "type": "number", + "minimum": 1 + }, + "lookback_period_candles": { + "description": "Period to look back for protection checks, in number of candles.", + "type": "number", + "minimum": 1 + } + }, + "required": [ + "method" + ] + } + }, + "telegram": { + "description": "Telegram settings.", + "type": "object", + "properties": { + "enabled": { + "description": "Enable Telegram notifications.", + "type": "boolean" + }, + "token": { + "description": "Telegram bot token.", + "type": "string" + }, + "chat_id": { + "description": "Telegram chat ID", + "type": "string" + }, + "allow_custom_messages": { + "description": "Allow sending custom messages from the Strategy.", + "type": "boolean", + "default": true + }, + "balance_dust_level": { + "description": "Minimum balance level to consider as dust.", + "type": "number", + "minimum": 0.0 + }, + "notification_settings": { + "description": "Settings for different types of notifications.", + "type": "object", + "default": {}, + "properties": { + "status": { + "description": "Telegram setting for status updates.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + }, + "warning": { + "description": "Telegram setting for warnings.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + }, + "startup": { + "description": "Telegram setting for startup messages.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + }, + "entry": { + "description": "Telegram setting for entry signals.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + }, + "entry_fill": { + "description": "Telegram setting for entry fill signals.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ], + "default": "off" + }, + "entry_cancel": { + "description": "Telegram setting for entry cancel signals.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + }, + "exit": { + "description": "Telegram setting for exit signals.", + "type": [ + "string", + "object" + ], + "additionalProperties": { + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + } + }, + "exit_fill": { + "description": "Telegram setting for exit fill signals.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ], + "default": "on" + }, + "exit_cancel": { + "description": "Telegram setting for exit cancel signals.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ] + }, + "protection_trigger": { + "description": "Telegram setting for protection triggers.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ], + "default": "on" + }, + "protection_trigger_global": { + "description": "Telegram setting for global protection triggers.", + "type": "string", + "enum": [ + "on", + "off", + "silent" + ], + "default": "on" + } + } + }, + "reload": { + "description": "Add Reload button to certain messages.", + "type": "boolean" + } + }, + "required": [ + "enabled", + "token", + "chat_id" + ] + }, + "webhook": { + "description": "Webhook settings.", + "type": "object", + "properties": { + "enabled": { + "type": "boolean" + }, + "url": { + "type": "string" + }, + "format": { + "type": "string", + "enum": [ + "form", + "json", + "raw" + ], + "default": "form" + }, + "retries": { + "type": "integer", + "minimum": 0 + }, + "retry_delay": { + "type": "number", + "minimum": 0 + }, + "status": { + "type": "object" + }, + "warning": { + "type": "object" + }, + "exception": { + "type": "object" + }, + "startup": { + "type": "object" + }, + "entry": { + "type": "object" + }, + "entry_fill": { + "type": "object" + }, + "entry_cancel": { + "type": "object" + }, + "exit": { + "type": "object" + }, + "exit_fill": { + "type": "object" + }, + "exit_cancel": { + "type": "object" + }, + "protection_trigger": { + "type": "object" + }, + "protection_trigger_global": { + "type": "object" + }, + "strategy_msg": { + "type": "object" + }, + "whitelist": { + "type": "object" + }, + "analyzed_df": { + "type": "object" + }, + "new_candle": { + "type": "object" + } + } + }, + "discord": { + "description": "Discord settings.", + "type": "object", + "properties": { + "enabled": { + "type": "boolean" + }, + "webhook_url": { + "type": "string" + }, + "exit_fill": { + "type": "array", + "items": { + "type": "object" + }, + "default": [ + { + "Trade ID": "{trade_id}" + }, + { + "Exchange": "{exchange}" + }, + { + "Pair": "{pair}" + }, + { + "Direction": "{direction}" + }, + { + "Open rate": "{open_rate}" + }, + { + "Close rate": "{close_rate}" + }, + { + "Amount": "{amount}" + }, + { + "Open date": "{open_date:%Y-%m-%d %H:%M:%S}" + }, + { + "Close date": "{close_date:%Y-%m-%d %H:%M:%S}" + }, + { + "Profit": "{profit_amount} {stake_currency}" + }, + { + "Profitability": "{profit_ratio:.2%}" + }, + { + "Enter tag": "{enter_tag}" + }, + { + "Exit Reason": "{exit_reason}" + }, + { + "Strategy": "{strategy}" + }, + { + "Timeframe": "{timeframe}" + } + ] + }, + "entry_fill": { + "type": "array", + "items": { + "type": "object" + }, + "default": [ + { + "Trade ID": "{trade_id}" + }, + { + "Exchange": "{exchange}" + }, + { + "Pair": "{pair}" + }, + { + "Direction": "{direction}" + }, + { + "Open rate": "{open_rate}" + }, + { + "Amount": "{amount}" + }, + { + "Open date": "{open_date:%Y-%m-%d %H:%M:%S}" + }, + { + "Enter tag": "{enter_tag}" + }, + { + "Strategy": "{strategy} {timeframe}" + } + ] + } + } + }, + "api_server": { + "description": "API server settings.", + "type": "object", + "properties": { + "enabled": { + "description": "Whether the API server is enabled.", + "type": "boolean" + }, + "listen_ip_address": { + "description": "IP address the API server listens on.", + "format": "ipv4" + }, + "listen_port": { + "description": "Port the API server listens on.", + "type": "integer", + "minimum": 1024, + "maximum": 65535 + }, + "username": { + "description": "Username for API server authentication.", + "type": "string" + }, + "password": { + "description": "Password for API server authentication.", + "type": "string" + }, + "ws_token": { + "description": "WebSocket token for API server.", + "type": [ + "string", + "array" + ], + "items": { + "type": "string" + } + }, + "jwt_secret_key": { + "description": "Secret key for JWT authentication.", + "type": "string" + }, + "CORS_origins": { + "description": "List of allowed CORS origins.", + "type": "array", + "items": { + "type": "string" + } + }, + "x": { + "description": "Logging verbosity level.", + "type": "string", + "enum": [ + "error", + "info" + ] + } + }, + "required": [ + "enabled", + "listen_ip_address", + "listen_port", + "username", + "password" + ] + }, + "db_url": { + "description": "Database connection URL.", + "type": "string" + }, + "export": { + "description": "Type of data to export.", + "type": "string", + "enum": [ + "none", + "trades", + "signals" + ], + "default": "trades" + }, + "disableparamexport": { + "description": "Disable parameter export.", + "type": "boolean" + }, + "initial_state": { + "description": "Initial state of the system.", + "type": "string", + "enum": [ + "running", + "stopped" + ] + }, + "force_entry_enable": { + "description": "Force enable entry.", + "type": "boolean" + }, + "disable_dataframe_checks": { + "description": "Disable checks on dataframes.", + "type": "boolean" + }, + "internals": { + "description": "Internal settings.", + "type": "object", + "default": {}, + "properties": { + "process_throttle_secs": { + "description": "Throttle time in seconds for processing.", + "type": "integer" + }, + "interval": { + "description": "Interval time in seconds.", + "type": "integer" + }, + "sd_notify": { + "description": "Enable systemd notify.", + "type": "boolean" + } + } + }, + "dataformat_ohlcv": { + "description": "Data format for OHLCV data.", + "type": "string", + "enum": [ + "json", + "jsongz", + "hdf5", + "feather", + "parquet" + ], + "default": "feather" + }, + "dataformat_trades": { + "description": "Data format for trade data.", + "type": "string", + "enum": [ + "json", + "jsongz", + "hdf5", + "feather", + "parquet" + ], + "default": "feather" + }, + "position_adjustment_enable": { + "description": "Enable position adjustment. \nUsually specified in the strategy and missing in the configuration.", + "type": "boolean" + }, + "max_entry_position_adjustment": { + "description": "Maximum entry position adjustment allowed. \nUsually specified in the strategy and missing in the configuration.", + "type": [ + "integer", + "number" + ], + "minimum": -1 + }, + "orderflow": { + "description": "Settings related to order flow.", + "type": "object", + "properties": { + "cache_size": { + "description": "Size of the cache for order flow data.", + "type": "number", + "minimum": 1, + "default": 1500 + }, + "max_candles": { + "description": "Maximum number of candles to consider.", + "type": "number", + "minimum": 1, + "default": 1500 + }, + "scale": { + "description": "Scale factor for order flow data.", + "type": "number", + "minimum": 0.0 + }, + "stacked_imbalance_range": { + "description": "Range for stacked imbalance.", + "type": "number", + "minimum": 0 + }, + "imbalance_volume": { + "description": "Volume threshold for imbalance.", + "type": "number", + "minimum": 0 + }, + "imbalance_ratio": { + "description": "Ratio threshold for imbalance.", + "type": "number", + "minimum": 0.0 + } + }, + "required": [ + "max_candles", + "scale", + "stacked_imbalance_range", + "imbalance_volume", + "imbalance_ratio" + ] + } + }, + "definitions": { + "exchange": { + "description": "Exchange configuration settings.", + "type": "object", + "properties": { + "name": { + "description": "Name of the exchange.", + "type": "string" + }, + "enable_ws": { + "description": "Enable WebSocket connections to the exchange.", + "type": "boolean", + "default": true + }, + "key": { + "description": "API key for the exchange.", + "type": "string", + "default": "" + }, + "secret": { + "description": "API secret for the exchange.", + "type": "string", + "default": "" + }, + "password": { + "description": "Password for the exchange, if required.", + "type": "string", + "default": "" + }, + "uid": { + "description": "User ID for the exchange, if required.", + "type": "string" + }, + "pair_whitelist": { + "description": "List of whitelisted trading pairs.", + "type": "array", + "items": { + "type": "string" + }, + "uniqueItems": true + }, + "pair_blacklist": { + "description": "List of blacklisted trading pairs.", + "type": "array", + "items": { + "type": "string" + }, + "uniqueItems": true + }, + "unknown_fee_rate": { + "description": "Fee rate for unknown markets.", + "type": "number" + }, + "outdated_offset": { + "description": "Offset for outdated data in minutes.", + "type": "integer", + "minimum": 1 + }, + "markets_refresh_interval": { + "description": "Interval for refreshing market data in minutes.", + "type": "integer", + "default": 60 + }, + "ccxt_config": { + "description": "CCXT configuration settings.", + "type": "object" + }, + "ccxt_async_config": { + "description": "CCXT asynchronous configuration settings.", + "type": "object" + } + }, + "required": [ + "name" + ] + }, + "edge": { + "type": "object", + "properties": { + "enabled": { + "type": "boolean" + }, + "process_throttle_secs": { + "type": "integer", + "minimum": 600 + }, + "calculate_since_number_of_days": { + "type": "integer" + }, + "allowed_risk": { + "type": "number" + }, + "stoploss_range_min": { + "type": "number" + }, + "stoploss_range_max": { + "type": "number" + }, + "stoploss_range_step": { + "type": "number" + }, + "minimum_winrate": { + "type": "number" + }, + "minimum_expectancy": { + "type": "number" + }, + "min_trade_number": { + "type": "number" + }, + "max_trade_duration_minute": { + "type": "integer" + }, + "remove_pumps": { + "type": "boolean" + } + }, + "required": [ + "process_throttle_secs", + "allowed_risk" + ] + }, + "external_message_consumer": { + "description": "Configuration for external message consumer.", + "type": "object", + "properties": { + "enabled": { + "description": "Whether the external message consumer is enabled.", + "type": "boolean", + "default": false + }, + "producers": { + "description": "List of producers for the external message consumer.", + "type": "array", + "items": { + "type": "object", + "properties": { + "name": { + "description": "Name of the producer.", + "type": "string" + }, + "host": { + "description": "Host of the producer.", + "type": "string" + }, + "port": { + "description": "Port of the producer.", + "type": "integer", + "default": 8080, + "minimum": 0, + "maximum": 65535 + }, + "secure": { + "description": "Whether to use SSL to connect to the producer.", + "type": "boolean", + "default": false + }, + "ws_token": { + "description": "WebSocket token for the producer.", + "type": "string" + } + }, + "required": [ + "name", + "host", + "ws_token" + ] + } + }, + "wait_timeout": { + "description": "Wait timeout in seconds.", + "type": "integer", + "minimum": 0 + }, + "sleep_time": { + "description": "Sleep time in seconds before retrying to connect.", + "type": "integer", + "minimum": 0 + }, + "ping_timeout": { + "description": "Ping timeout in seconds.", + "type": "integer", + "minimum": 0 + }, + "remove_entry_exit_signals": { + "description": "Remove signal columns from the dataframe (set them to 0)", + "type": "boolean", + "default": false + }, + "initial_candle_limit": { + "description": "Initial candle limit.", + "type": "integer", + "minimum": 0, + "maximum": 1500, + "default": 1500 + }, + "message_size_limit": { + "description": "Message size limit in megabytes.", + "type": "integer", + "minimum": 1, + "maximum": 20, + "default": 8 + } + }, + "required": [ + "producers" + ] + }, + "freqai": { + "type": "object", + "properties": { + "enabled": { + "description": "Whether freqAI is enabled.", + "type": "boolean", + "default": false + }, + "keras": { + "description": "Use Keras for model training.", + "type": "boolean", + "default": false + }, + "write_metrics_to_disk": { + "description": "Write metrics to disk?", + "type": "boolean", + "default": false + }, + "purge_old_models": { + "description": "Number of models to keep on disk.", + "type": [ + "boolean", + "number" + ], + "default": 2 + }, + "conv_width": { + "description": "The width of a neural network input tensor.", + "type": "integer", + "default": 1 + }, + "train_period_days": { + "description": "Number of days to use for the training data (width of the sliding window)", + "type": "integer", + "default": 0 + }, + "backtest_period_days": { + "description": "Number of days to inference from the trained model before sliding the `train_period_days` window ", + "type": "number", + "default": 7 + }, + "identifier": { + "description": "A unique ID for the current model. Must be changed when modifying features.", + "type": "string", + "default": "example" + }, + "feature_parameters": { + "description": "The parameters used to engineer the feature set", + "type": "object", + "properties": { + "include_corr_pairlist": { + "description": "List of correlated pairs to include in the features.", + "type": "array" + }, + "include_timeframes": { + "description": "A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for.", + "type": "array" + }, + "label_period_candles": { + "description": "Number of candles into the future to use for labeling the period.This can be used in `set_freqai_targets()`.", + "type": "integer" + }, + "include_shifted_candles": { + "description": "Add features from previous candles to subsequent candles with the intent of adding historical information.", + "type": "integer", + "default": 0 + }, + "DI_threshold": { + "description": "Activates the use of the Dissimilarity Index for outlier detection when set to > 0.", + "type": "number", + "default": 0 + }, + "weight_factor": { + "description": "Weight training data points according to their recency.", + "type": "number", + "default": 0 + }, + "principal_component_analysis": { + "description": "Automatically reduce the dimensionality of the data set using Principal Component Analysis", + "type": "boolean", + "default": false + }, + "use_SVM_to_remove_outliers": { + "description": "Use SVM to remove outliers from the features.", + "type": "boolean", + "default": false + }, + "plot_feature_importances": { + "description": "Create feature importance plots for each model.", + "type": "integer", + "default": 0 + }, + "svm_params": { + "description": "All parameters available in Sklearn's `SGDOneClassSVM()`.", + "type": "object", + "properties": { + "shuffle": { + "description": "Whether to shuffle data before applying SVM.", + "type": "boolean", + "default": false + }, + "nu": { + "type": "number", + "default": 0.1 + } + } + }, + "shuffle_after_split": { + "description": "Split the data into train and test sets, and then shuffle both sets individually.", + "type": "boolean", + "default": false + }, + "buffer_train_data_candles": { + "description": "Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated.", + "type": "integer", + "default": 0 + } + }, + "required": [ + "include_timeframes", + "include_corr_pairlist" + ] + }, + "data_split_parameters": { + "descriptions": "Additional parameters for scikit-learn's test_train_split() function.", + "type": "object", + "properties": { + "test_size": { + "type": "number" + }, + "random_state": { + "type": "integer" + }, + "shuffle": { + "type": "boolean", + "default": false + } + } + }, + "model_training_parameters": { + "description": "Flexible dictionary that includes all parameters available by the selected model library. ", + "type": "object" + }, + "rl_config": { + "type": "object", + "properties": { + "drop_ohlc_from_features": { + "description": "Do not include the normalized ohlc data in the feature set.", + "type": "boolean", + "default": false + }, + "train_cycles": { + "description": "Number of training cycles to perform.", + "type": "integer" + }, + "max_trade_duration_candles": { + "description": "Guides the agent training to keep trades below desired length.", + "type": "integer" + }, + "add_state_info": { + "description": "Include state information in the feature set for training and inference.", + "type": "boolean", + "default": false + }, + "max_training_drawdown_pct": { + "description": "Maximum allowed drawdown percentage during training.", + "type": "number", + "default": 0.02 + }, + "cpu_count": { + "description": "Number of threads/CPU's to use for training.", + "type": "integer", + "default": 1 + }, + "model_type": { + "description": "Model string from stable_baselines3 or SBcontrib.", + "type": "string", + "default": "PPO" + }, + "policy_type": { + "description": "One of the available policy types from stable_baselines3.", + "type": "string", + "default": "MlpPolicy" + }, + "net_arch": { + "description": "Architecture of the neural network.", + "type": "array", + "default": [ + 128, + 128 + ] + }, + "randomize_starting_position": { + "description": "Randomize the starting point of each episode to avoid overfitting.", + "type": "boolean", + "default": false + }, + "progress_bar": { + "description": "Display a progress bar with the current progress.", + "type": "boolean", + "default": true + }, + "model_reward_parameters": { + "description": "Parameters for configuring the reward model.", + "type": "object", + "properties": { + "rr": { + "type": "number", + "default": 1, + "description": "Reward ratio parameter." + }, + "profit_aim": { + "type": "number", + "default": 0.025, + "description": "Profit aim parameter." + } + } + } + } + } + }, + "required": [ + "enabled", + "train_period_days", + "backtest_period_days", + "identifier", + "feature_parameters", + "data_split_parameters" + ] + } + } +} \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 3b6f45bfc..6b7eefab7 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,5 +1,4 @@ --- -version: '3' services: freqtrade: image: freqtradeorg/freqtrade:stable diff --git a/docker/docker-compose-freqai.yml b/docker/docker-compose-freqai.yml index ce493d5b7..f61548734 100644 --- a/docker/docker-compose-freqai.yml +++ b/docker/docker-compose-freqai.yml @@ -1,5 +1,4 @@ --- -version: '3' services: freqtrade: image: freqtradeorg/freqtrade:stable_freqaitorch diff --git a/docker/docker-compose-jupyter.yml b/docker/docker-compose-jupyter.yml index b72c8df84..e8d3629c7 100644 --- a/docker/docker-compose-jupyter.yml +++ b/docker/docker-compose-jupyter.yml @@ -1,5 +1,4 @@ --- -version: '3' services: ft_jupyterlab: build: diff --git a/docs/advanced-orderflow.md b/docs/advanced-orderflow.md new file mode 100644 index 000000000..22a28fe87 --- /dev/null +++ b/docs/advanced-orderflow.md @@ -0,0 +1,152 @@ +# Orderflow data + +This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade. + +!!! Warning "Experimental Feature" + The orderflow feature is currently in beta and may be subject to changes in future releases. Please report any issues or feedback on the [Freqtrade GitHub repository](https://github.com/freqtrade/freqtrade/issues). + +!!! Warning "Performance" + Orderflow requires raw trades data. This data is rather large, and can cause a slow initial startup, when freqtrade needs to download the trades data for the last X candles. Additionally, enabling this feature will cause increased memory usage. Please ensure to have sufficient resources available. + +## Getting Started + +### Enable Public Trades + +In your `config.json` file, set the `use_public_trades` option to true under the `exchange` section. + +```json +"exchange": { + ... + "use_public_trades": true, +} +``` + +### Configure Orderflow Processing + +Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like: + +- `cache_size`: How many previous orderflow candles are saved into cache instead of calculated every new candle +- `max_candles`: Filter how many candles would you like to get trades data for. +- `scale`: This controls the price bin size for the footprint chart. +- `stacked_imbalance_range`: Defines the minimum consecutive imbalanced price levels required for consideration. +- `imbalance_volume`: Filters out imbalances with volume below this threshold. +- `imbalance_ratio`: Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value. + +```json +"orderflow": { + "cache_size": 1000, + "max_candles": 1500, + "scale": 0.5, + "stacked_imbalance_range": 3, // needs at least this amount of imbalance next to each other + "imbalance_volume": 1, // filters out below + "imbalance_ratio": 3 // filters out ratio lower than + }, +``` + +## Downloading Trade Data for Backtesting + +To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command. + +```bash +freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades +``` + +!!! Warning "Data availability" + Not all exchanges provide public trade data. For supported exchanges, freqtrade will warn you if public trade data is not available if you start downloading data with the `--dl-trades` flag. + +## Accessing Orderflow Data + +Once activated, several new columns become available in your dataframe: + +``` python + +dataframe["trades"] # Contains information about each individual trade. +dataframe["orderflow"] # Represents a footprint chart dict (see below) +dataframe["imbalances"] # Contains information about imbalances in the order flow. +dataframe["bid"] # Total bid volume +dataframe["ask"] # Total ask volume +dataframe["delta"] # Difference between ask and bid volume. +dataframe["min_delta"] # Minimum delta within the candle +dataframe["max_delta"] # Maximum delta within the candle +dataframe["total_trades"] # Total number of trades +dataframe["stacked_imbalances_bid"] # Price level of stacked bid imbalance +dataframe["stacked_imbalances_ask"] # Price level of stacked ask imbalance +``` + +You can access these columns in your strategy code for further analysis. Here's an example: + +``` python +def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + # Calculating cumulative delta + dataframe["cum_delta"] = cumulative_delta(dataframe["delta"]) + # Accessing total trades + total_trades = dataframe["total_trades"] + ... + +def cumulative_delta(delta: Series): + cumdelta = delta.cumsum() + return cumdelta + +``` + +### Footprint chart (`dataframe["orderflow"]`) + +This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The `scale` parameter in your configuration determines the price bin size for this representation + +The `orderflow` column contains a dict with the following structure: + +``` output +{ + "price": { + "bid_amount": 0.0, + "ask_amount": 0.0, + "bid": 0, + "ask": 0, + "delta": 0.0, + "total_volume": 0.0, + "total_trades": 0 + } +} +``` + +#### Orderflow column explanation + +- key: Price bin - binned at `scale` intervals +- `bid_amount`: Total volume bought at each price level. +- `ask_amount`: Total volume sold at each price level. +- `bid`: Number of buy orders at each price level. +- `ask`: Number of sell orders at each price level. +- `delta`: Difference between ask and bid volume at each price level. +- `total_volume`: Total volume (ask amount + bid amount) at each price level. +- `total_trades`: Total number of trades (ask + bid) at each price level. + +By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis. + +### Raw trades data (`dataframe["trades"]`) + +List with the individual trades that occurred during the candle. This data can be used for more granular analysis of order flow dynamics. + +Each individual entry contains a dict with the following keys: + +- `timestamp`: Timestamp of the trade. +- `date`: Date of the trade. +- `price`: Price of the trade. +- `amount`: Volume of the trade. +- `side`: Buy or sell. +- `id`: Unique identifier for the trade. +- `cost`: Total cost of the trade (price * amount). + +### Imbalances (`dataframe["imbalances"]`) + +This column provides a dict with information about imbalances in the order flow. An imbalance occurs when there is a significant difference between the ask and bid volume at a given price level. + +Each row looks as follows - with price as index, and the corresponding bid and ask imbalance values as columns + +``` output +{ + "price": { + "bid_imbalance": False, + "ask_imbalance": False + } +} +``` diff --git a/docs/advanced-setup.md b/docs/advanced-setup.md index 54d489aa1..ae2304538 100644 --- a/docs/advanced-setup.md +++ b/docs/advanced-setup.md @@ -114,8 +114,46 @@ services: --strategy SampleStrategy ``` + You can use whatever naming convention you want, freqtrade1 and 2 are arbitrary. Note, that you will need to use different database files, port mappings and telegram configurations for each instance, as mentioned above. +## Use a different database system + +Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported. +Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems. + +The following systems have been tested and are known to work with freqtrade: + +* sqlite (default) +* PostgreSQL +* MariaDB + +!!! Warning + By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems. + +### PostgreSQL + +Installation: +`pip install psycopg2-binary` + +Usage: +`... --db-url postgresql+psycopg2://:@localhost:5432/` + +Freqtrade will automatically create the tables necessary upon startup. + +If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections. + +### MariaDB / MySQL + +Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems. + +Installation: +`pip install pymysql` + +Usage: +`... --db-url mysql+pymysql://:@localhost:3306/` + + ## Configure the bot running as a systemd service diff --git a/docs/freqai-running.md b/docs/freqai-running.md index 1b721b658..d506ac3ce 100644 --- a/docs/freqai-running.md +++ b/docs/freqai-running.md @@ -83,6 +83,10 @@ To change your **features**, you **must** set a new `identifier` in the config t To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config. +!!! Note + To ensure that the model can be reused, freqAI will call your strategy with a dataframe of length 1. + If your strategy requires more data than this to generate the same features, you can't reuse backtest predictions for live deployment and need to update your `identifier` for each new backtest. + ### Backtest live collected predictions FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study. diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index 27c1a125e..20beace35 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,6 +1,6 @@ markdown==3.6 mkdocs==1.6.0 -mkdocs-material==9.5.28 +mkdocs-material==9.5.29 mdx_truly_sane_lists==1.3 pymdown-extensions==10.8.1 jinja2==3.1.4 diff --git a/docs/rest-api.md b/docs/rest-api.md index 3b5c8928f..8c927d5d7 100644 --- a/docs/rest-api.md +++ b/docs/rest-api.md @@ -2,7 +2,7 @@ ## FreqUI -FreqUI now has it's own dedicated [documentation section](frequi.md) - please refer to that section for all information regarding the FreqUI. +FreqUI now has it's own dedicated [documentation section](freq-ui.md) - please refer to that section for all information regarding the FreqUI. ## Configuration diff --git a/docs/sql_cheatsheet.md b/docs/sql_cheatsheet.md index a0c5c8da1..21e03d695 100644 --- a/docs/sql_cheatsheet.md +++ b/docs/sql_cheatsheet.md @@ -1,6 +1,13 @@ # SQL Helper -This page contains some help if you want to edit your sqlite db. +This page contains some help if you want to query your sqlite db. + +!!! Tip "Other Database systems" + To use other Database Systems like PostgreSQL or MariaDB, you can use the same queries, but you need to use the respective client for the database system. [Click here](advanced-setup.md#use-a-different-database-system) to learn how to setup a different database system with freqtrade. + +!!! Warning + If you are not familiar with SQL, you should be very careful when running queries on your database. + Always make sure to have a backup of your database before running any queries. ## Install sqlite3 @@ -43,13 +50,25 @@ sqlite3 .schema ``` -## Get all trades in the table +### Get all trades in the table ```sql SELECT * FROM trades; ``` -## Fix trade still open after a manual exit on the exchange +## Destructive queries + +Queries that write to the database. +These queries should usually not be necessary as freqtrade tries to handle all database operations itself - or exposes them via API or telegram commands. + +!!! Warning + Please make sure you have a backup of your database before running any of the below queries. + +!!! Danger + You should also **never** run any writing query (`update`, `insert`, `delete`) while a bot is connected to the database. + This can and will lead to data corruption - most likely, without the possibility of recovery. + +### Fix trade still open after a manual exit on the exchange !!! Warning Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, /forceexit should be used to accomplish the same thing. @@ -69,7 +88,7 @@ SET is_open=0, WHERE id=; ``` -### Example +#### Example ```sql UPDATE trades @@ -82,7 +101,7 @@ SET is_open=0, WHERE id=31; ``` -## Remove trade from the database +### Remove trade from the database !!! Tip "Use RPC Methods to delete trades" Consider using `/delete ` via telegram or rest API. That's the recommended way to deleting trades. @@ -100,39 +119,3 @@ DELETE FROM trades WHERE id = 31; !!! Warning This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause. - -## Use a different database system - -Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported. -Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems. - -The following systems have been tested and are known to work with freqtrade: - -* sqlite (default) -* PostgreSQL -* MariaDB - -!!! Warning - By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems. - -### PostgreSQL - -Installation: -`pip install psycopg2-binary` - -Usage: -`... --db-url postgresql+psycopg2://:@localhost:5432/` - -Freqtrade will automatically create the tables necessary upon startup. - -If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections. - -### MariaDB / MySQL - -Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems. - -Installation: -`pip install pymysql` - -Usage: -`... --db-url mysql+pymysql://:@localhost:3306/` diff --git a/docs/utils.md b/docs/utils.md index a11e7b698..78688a7f7 100644 --- a/docs/utils.md +++ b/docs/utils.md @@ -488,7 +488,7 @@ freqtrade test-pairlist --config config.json --quote USDT BTC `freqtrade convert-db` can be used to convert your database from one system to another (sqlite -> postgres, postgres -> other postgres), migrating all trades, orders and Pairlocks. -Please refer to the [SQL cheatsheet](sql_cheatsheet.md#use-a-different-database-system) to learn about requirements for different database systems. +Please refer to the [corresponding documentation](advanced-setup.md#use-a-different-database-system) to learn about requirements for different database systems. ``` usage: freqtrade convert-db [-h] [--db-url PATH] [--db-url-from PATH] diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 92e60daa4..f3f56c7b2 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -16,6 +16,7 @@ from freqtrade.exceptions import ConfigurationError from freqtrade.exchange import timeframe_to_minutes from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist from freqtrade.resolvers import ExchangeResolver +from freqtrade.util import print_rich_table from freqtrade.util.migrations import migrate_data @@ -119,8 +120,6 @@ def start_list_data(args: Dict[str, Any]) -> None: config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE) - from tabulate import tabulate - from freqtrade.data.history import get_datahandler dhc = get_datahandler(config["datadir"], config["dataformat_ohlcv"]) @@ -131,8 +130,7 @@ def start_list_data(args: Dict[str, Any]) -> None: if args["pairs"]: paircombs = [comb for comb in paircombs if comb[0] in args["pairs"]] - - print(f"Found {len(paircombs)} pair / timeframe combinations.") + title = f"Found {len(paircombs)} pair / timeframe combinations." if not config.get("show_timerange"): groupedpair = defaultdict(list) for pair, timeframe, candle_type in sorted( @@ -141,40 +139,35 @@ def start_list_data(args: Dict[str, Any]) -> None: groupedpair[(pair, candle_type)].append(timeframe) if groupedpair: - print( - tabulate( - [ - (pair, ", ".join(timeframes), candle_type) - for (pair, candle_type), timeframes in groupedpair.items() - ], - headers=("Pair", "Timeframe", "Type"), - tablefmt="psql", - stralign="right", - ) + print_rich_table( + [ + (pair, ", ".join(timeframes), candle_type) + for (pair, candle_type), timeframes in groupedpair.items() + ], + ("Pair", "Timeframe", "Type"), + title, + table_kwargs={"min_width": 50}, ) else: paircombs1 = [ (pair, timeframe, candle_type, *dhc.ohlcv_data_min_max(pair, timeframe, candle_type)) for pair, timeframe, candle_type in paircombs ] - - print( - tabulate( - [ - ( - pair, - timeframe, - candle_type, - start.strftime(DATETIME_PRINT_FORMAT), - end.strftime(DATETIME_PRINT_FORMAT), - length, - ) - for pair, timeframe, candle_type, start, end, length in sorted( - paircombs1, key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]) - ) - ], - headers=("Pair", "Timeframe", "Type", "From", "To", "Candles"), - tablefmt="psql", - stralign="right", - ) + print_rich_table( + [ + ( + pair, + timeframe, + candle_type, + start.strftime(DATETIME_PRINT_FORMAT), + end.strftime(DATETIME_PRINT_FORMAT), + str(length), + ) + for pair, timeframe, candle_type, start, end, length in sorted( + paircombs1, key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]) + ) + ], + ("Pair", "Timeframe", "Type", "From", "To", "Candles"), + summary=title, + table_kwargs={"min_width": 50}, ) diff --git a/freqtrade/commands/hyperopt_commands.py b/freqtrade/commands/hyperopt_commands.py index ac0b8453f..d89d25796 100644 --- a/freqtrade/commands/hyperopt_commands.py +++ b/freqtrade/commands/hyperopt_commands.py @@ -2,8 +2,6 @@ import logging from operator import itemgetter from typing import Any, Dict -from colorama import init as colorama_init - from freqtrade.configuration import setup_utils_configuration from freqtrade.data.btanalysis import get_latest_hyperopt_file from freqtrade.enums import RunMode @@ -18,6 +16,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None: """ List hyperopt epochs previously evaluated """ + from freqtrade.optimize.hyperopt_output import HyperoptOutput from freqtrade.optimize.hyperopt_tools import HyperoptTools config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE) @@ -35,21 +34,17 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None: # Previous evaluations epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config) - if print_colorized: - colorama_init(autoreset=True) - if not export_csv: try: - print( - HyperoptTools.get_result_table( - config, - epochs, - total_epochs, - not config.get("hyperopt_list_best", False), - print_colorized, - 0, - ) + h_out = HyperoptOutput() + h_out.add_data( + config, + epochs, + total_epochs, + not config.get("hyperopt_list_best", False), ) + h_out.print(print_colorized=print_colorized) + except KeyboardInterrupt: print("User interrupted..") diff --git a/freqtrade/commands/list_commands.py b/freqtrade/commands/list_commands.py index 97be7bac6..1696fc8f0 100644 --- a/freqtrade/commands/list_commands.py +++ b/freqtrade/commands/list_commands.py @@ -4,9 +4,9 @@ import sys from typing import Any, Dict, List, Union import rapidjson -from colorama import Fore, Style -from colorama import init as colorama_init -from tabulate import tabulate +from rich.console import Console +from rich.table import Table +from rich.text import Text from freqtrade.configuration import setup_utils_configuration from freqtrade.enums import RunMode @@ -14,7 +14,8 @@ from freqtrade.exceptions import ConfigurationError, OperationalException from freqtrade.exchange import list_available_exchanges, market_is_active from freqtrade.misc import parse_db_uri_for_logging, plural from freqtrade.resolvers import ExchangeResolver, StrategyResolver -from freqtrade.types import ValidExchangesType +from freqtrade.types.valid_exchanges_type import ValidExchangesType +from freqtrade.util import print_rich_table logger = logging.getLogger(__name__) @@ -26,72 +27,69 @@ def start_list_exchanges(args: Dict[str, Any]) -> None: :param args: Cli args from Arguments() :return: None """ - exchanges = list_available_exchanges(args["list_exchanges_all"]) + available_exchanges: List[ValidExchangesType] = list_available_exchanges( + args["list_exchanges_all"] + ) if args["print_one_column"]: - print("\n".join([e["name"] for e in exchanges])) + print("\n".join([e["name"] for e in available_exchanges])) else: - headers = { - "name": "Exchange name", - "supported": "Supported", - "trade_modes": "Markets", - "comment": "Reason", - } - headers.update({"valid": "Valid"} if args["list_exchanges_all"] else {}) + if args["list_exchanges_all"]: + title = ( + f"All exchanges supported by the ccxt library " + f"({len(available_exchanges)} exchanges):" + ) + else: + available_exchanges = [e for e in available_exchanges if e["valid"] is not False] + title = f"Exchanges available for Freqtrade ({len(available_exchanges)} exchanges):" - def build_entry(exchange: ValidExchangesType, valid: bool): - valid_entry = {"valid": exchange["valid"]} if valid else {} - result: Dict[str, Union[str, bool]] = { - "name": exchange["name"], - **valid_entry, - "supported": "Official" if exchange["supported"] else "", - "trade_modes": ("DEX: " if exchange["dex"] else "") - + ", ".join( - (f"{a['margin_mode']} " if a["margin_mode"] else "") + a["trading_mode"] + table = Table(title=title) + + table.add_column("Exchange Name") + table.add_column("Markets") + table.add_column("Reason") + + for exchange in available_exchanges: + name = Text(exchange["name"]) + if exchange["supported"]: + name.append(" (Official)", style="italic") + name.stylize("green bold") + + trade_modes = Text( + ", ".join( + (f"{a.get('margin_mode', '')} {a['trading_mode']}").lstrip() for a in exchange["trade_modes"] ), - "comment": exchange["comment"], - } - - return result - - if args["list_exchanges_all"]: - exchanges = [build_entry(e, True) for e in exchanges] - print(f"All exchanges supported by the ccxt library ({len(exchanges)} exchanges):") - else: - exchanges = [build_entry(e, False) for e in exchanges if e["valid"] is not False] - print(f"Exchanges available for Freqtrade ({len(exchanges)} exchanges):") - - print( - tabulate( - exchanges, - headers=headers, + style="", ) - ) + if exchange["dex"]: + trade_modes = Text("DEX: ") + trade_modes + trade_modes.stylize("bold", 0, 3) + + table.add_row( + name, + trade_modes, + exchange["comment"], + style=None if exchange["valid"] else "red", + ) + # table.add_row(*[exchange[header] for header in headers]) + + console = Console() + console.print(table) def _print_objs_tabular(objs: List, print_colorized: bool) -> None: - if print_colorized: - colorama_init(autoreset=True) - red = Fore.RED - yellow = Fore.YELLOW - reset = Style.RESET_ALL - else: - red = "" - yellow = "" - reset = "" - names = [s["name"] for s in objs] - objs_to_print = [ + objs_to_print: List[Dict[str, Union[Text, str]]] = [ { - "name": s["name"] if s["name"] else "--", + "name": Text(s["name"] if s["name"] else "--"), "location": s["location_rel"], "status": ( - red + "LOAD FAILED" + reset + Text("LOAD FAILED", style="bold red") if s["class"] is None - else "OK" + else Text("OK", style="bold green") if names.count(s["name"]) == 1 - else yellow + "DUPLICATE NAME" + reset + else Text("DUPLICATE NAME", style="bold yellow") ), } for s in objs @@ -101,11 +99,23 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None: objs_to_print[idx].update( { "hyperoptable": "Yes" if s["hyperoptable"]["count"] > 0 else "No", - "buy-Params": len(s["hyperoptable"].get("buy", [])), - "sell-Params": len(s["hyperoptable"].get("sell", [])), + "buy-Params": str(len(s["hyperoptable"].get("buy", []))), + "sell-Params": str(len(s["hyperoptable"].get("sell", []))), } ) - print(tabulate(objs_to_print, headers="keys", tablefmt="psql", stralign="right")) + table = Table() + + for header in objs_to_print[0].keys(): + table.add_column(header.capitalize(), justify="right") + + for row in objs_to_print: + table.add_row(*[row[header] for header in objs_to_print[0].keys()]) + + console = Console( + color_system="auto" if print_colorized else None, + width=200 if "pytest" in sys.modules else None, + ) + console.print(table) def start_list_strategies(args: Dict[str, Any]) -> None: @@ -270,9 +280,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None: writer.writeheader() writer.writerows(tabular_data) else: - # print data as a table, with the human-readable summary - print(f"{summary_str}:") - print(tabulate(tabular_data, headers="keys", tablefmt="psql", stralign="right")) + print_rich_table(tabular_data, headers, summary_str) elif not ( args.get("print_one_column", False) or args.get("list_pairs_print_json", False) diff --git a/freqtrade/configuration/config_schema.py b/freqtrade/configuration/config_schema.py new file mode 100644 index 000000000..7beed69e6 --- /dev/null +++ b/freqtrade/configuration/config_schema.py @@ -0,0 +1,1286 @@ +# Required json-schema for user specified config +from typing import Dict + +from freqtrade.constants import ( + AVAILABLE_DATAHANDLERS, + AVAILABLE_PAIRLISTS, + AVAILABLE_PROTECTIONS, + BACKTEST_BREAKDOWNS, + DRY_RUN_WALLET, + EXPORT_OPTIONS, + MARGIN_MODES, + ORDERTIF_POSSIBILITIES, + ORDERTYPE_POSSIBILITIES, + PRICING_SIDES, + REQUIRED_ORDERTIF, + STOPLOSS_PRICE_TYPES, + SUPPORTED_FIAT, + TELEGRAM_SETTING_OPTIONS, + TIMEOUT_UNITS, + TRADING_MODES, + UNLIMITED_STAKE_AMOUNT, + WEBHOOK_FORMAT_OPTIONS, +) +from freqtrade.enums import RPCMessageType + + +__MESSAGE_TYPE_DICT: Dict[str, Dict[str, str]] = {x: {"type": "object"} for x in RPCMessageType} + +__IN_STRATEGY = "\nUsually specified in the strategy and missing in the configuration." + +CONF_SCHEMA = { + "type": "object", + "properties": { + "max_open_trades": { + "description": "Maximum number of open trades. -1 for unlimited.", + "type": ["integer", "number"], + "minimum": -1, + }, + "new_pairs_days": { + "description": "Download data of new pairs for given number of days", + "type": "integer", + "default": 30, + }, + "timeframe": { + "description": ( + f"The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). {__IN_STRATEGY}" + ), + "type": "string", + }, + "stake_currency": { + "description": "Currency used for staking.", + "type": "string", + }, + "stake_amount": { + "description": "Amount to stake per trade.", + "type": ["number", "string"], + "minimum": 0.0001, + "pattern": UNLIMITED_STAKE_AMOUNT, + }, + "tradable_balance_ratio": { + "description": "Ratio of balance that is tradable.", + "type": "number", + "minimum": 0.0, + "maximum": 1, + "default": 0.99, + }, + "available_capital": { + "description": "Total capital available for trading.", + "type": "number", + "minimum": 0, + }, + "amend_last_stake_amount": { + "description": "Whether to amend the last stake amount.", + "type": "boolean", + "default": False, + }, + "last_stake_amount_min_ratio": { + "description": "Minimum ratio for the last stake amount.", + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.5, + }, + "fiat_display_currency": { + "description": "Fiat currency for display purposes.", + "type": "string", + "enum": SUPPORTED_FIAT, + }, + "dry_run": { + "description": "Enable or disable dry run mode.", + "type": "boolean", + }, + "dry_run_wallet": { + "description": "Initial wallet balance for dry run mode.", + "type": "number", + "default": DRY_RUN_WALLET, + }, + "cancel_open_orders_on_exit": { + "description": "Cancel open orders when exiting.", + "type": "boolean", + "default": False, + }, + "process_only_new_candles": { + "description": "Process only new candles.", + "type": "boolean", + }, + "minimal_roi": { + "description": f"Minimum return on investment. {__IN_STRATEGY}", + "type": "object", + "patternProperties": {"^[0-9.]+$": {"type": "number"}}, + }, + "amount_reserve_percent": { + "description": "Percentage of amount to reserve.", + "type": "number", + "minimum": 0.0, + "maximum": 0.5, + }, + "stoploss": { + "description": f"Value (as ratio) to use as Stoploss value. {__IN_STRATEGY}", + "type": "number", + "maximum": 0, + "exclusiveMaximum": True, + }, + "trailing_stop": { + "description": f"Enable or disable trailing stop. {__IN_STRATEGY}", + "type": "boolean", + }, + "trailing_stop_positive": { + "description": f"Positive offset for trailing stop. {__IN_STRATEGY}", + "type": "number", + "minimum": 0, + "maximum": 1, + }, + "trailing_stop_positive_offset": { + "description": f"Offset for trailing stop to activate. {__IN_STRATEGY}", + "type": "number", + "minimum": 0, + "maximum": 1, + }, + "trailing_only_offset_is_reached": { + "description": f"Use trailing stop only when offset is reached. {__IN_STRATEGY}", + "type": "boolean", + }, + "use_exit_signal": { + "description": f"Use exit signal for trades. {__IN_STRATEGY}", + "type": "boolean", + }, + "exit_profit_only": { + "description": ( + "Exit only when in profit. Exit signals are ignored as " + f"long as profit is < exit_profit_offset. {__IN_STRATEGY}" + ), + "type": "boolean", + }, + "exit_profit_offset": { + "description": f"Offset for profit exit. {__IN_STRATEGY}", + "type": "number", + }, + "fee": { + "description": "Trading fee percentage. Can help to simulate slippage in backtesting", + "type": "number", + "minimum": 0, + "maximum": 0.1, + }, + "ignore_roi_if_entry_signal": { + "description": f"Ignore ROI if entry signal is present. {__IN_STRATEGY}", + "type": "boolean", + }, + "ignore_buying_expired_candle_after": { + "description": f"Ignore buying after candle expiration time. {__IN_STRATEGY}", + "type": "number", + }, + "trading_mode": { + "description": "Mode of trading (e.g., spot, margin).", + "type": "string", + "enum": TRADING_MODES, + }, + "margin_mode": { + "description": "Margin mode for trading.", + "type": "string", + "enum": MARGIN_MODES, + }, + "reduce_df_footprint": { + "description": "Reduce DataFrame footprint by casting columns to float32/int32.", + "type": "boolean", + "default": False, + }, + "minimum_trade_amount": { + "description": "Minimum amount for a trade - only used for lookahead-analysis", + "type": "number", + "default": 10, + }, + "targeted_trade_amount": { + "description": "Targeted trade amount for lookahead analysis.", + "type": "number", + "default": 20, + }, + "lookahead_analysis_exportfilename": { + "description": "csv Filename for lookahead analysis export.", + "type": "string", + }, + "startup_candle": { + "description": "Startup candle configuration.", + "type": "array", + "uniqueItems": True, + "default": [199, 399, 499, 999, 1999], + }, + "liquidation_buffer": { + "description": "Buffer ratio for liquidation.", + "type": "number", + "minimum": 0.0, + "maximum": 0.99, + }, + "backtest_breakdown": { + "description": "Breakdown configuration for backtesting.", + "type": "array", + "items": {"type": "string", "enum": BACKTEST_BREAKDOWNS}, + }, + "bot_name": { + "description": "Name of the trading bot. Passed via API to a client.", + "type": "string", + }, + "unfilledtimeout": { + "description": f"Timeout configuration for unfilled orders. {__IN_STRATEGY}", + "type": "object", + "properties": { + "entry": { + "description": "Timeout for entry orders in unit.", + "type": "number", + "minimum": 1, + }, + "exit": { + "description": "Timeout for exit orders in unit.", + "type": "number", + "minimum": 1, + }, + "exit_timeout_count": { + "description": "Number of times to retry exit orders before giving up.", + "type": "number", + "minimum": 0, + "default": 0, + }, + "unit": { + "description": "Unit of time for the timeout (e.g., seconds, minutes).", + "type": "string", + "enum": TIMEOUT_UNITS, + "default": "minutes", + }, + }, + }, + "entry_pricing": { + "description": "Configuration for entry pricing.", + "type": "object", + "properties": { + "price_last_balance": { + "description": "Balance ratio for the last price.", + "type": "number", + "minimum": 0, + "maximum": 1, + "exclusiveMaximum": False, + }, + "price_side": { + "description": "Side of the price to use (e.g., bid, ask, same).", + "type": "string", + "enum": PRICING_SIDES, + "default": "same", + }, + "use_order_book": { + "description": "Whether to use the order book for pricing.", + "type": "boolean", + }, + "order_book_top": { + "description": "Top N levels of the order book to consider.", + "type": "integer", + "minimum": 1, + "maximum": 50, + }, + "check_depth_of_market": { + "description": "Configuration for checking the depth of the market.", + "type": "object", + "properties": { + "enabled": { + "description": "Enable or disable depth of market check.", + "type": "boolean", + }, + "bids_to_ask_delta": { + "description": "Delta between bids and asks to consider.", + "type": "number", + "minimum": 0, + }, + }, + }, + }, + "required": ["price_side"], + }, + "exit_pricing": { + "description": "Configuration for exit pricing.", + "type": "object", + "properties": { + "price_side": { + "description": "Side of the price to use (e.g., bid, ask, same).", + "type": "string", + "enum": PRICING_SIDES, + "default": "same", + }, + "price_last_balance": { + "description": "Balance ratio for the last price.", + "type": "number", + "minimum": 0, + "maximum": 1, + "exclusiveMaximum": False, + }, + "use_order_book": { + "description": "Whether to use the order book for pricing.", + "type": "boolean", + }, + "order_book_top": { + "description": "Top N levels of the order book to consider.", + "type": "integer", + "minimum": 1, + "maximum": 50, + }, + }, + "required": ["price_side"], + }, + "custom_price_max_distance_ratio": { + "description": "Maximum distance ratio between current and custom entry or exit price.", + "type": "number", + "minimum": 0.0, + "maximum": 1, + "default": 0.02, + }, + "order_types": { + "description": f"Configuration of order types. {__IN_STRATEGY}", + "type": "object", + "properties": { + "entry": { + "description": "Order type for entry (e.g., limit, market).", + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + }, + "exit": { + "description": "Order type for exit (e.g., limit, market).", + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + }, + "force_exit": { + "description": "Order type for forced exit (e.g., limit, market).", + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + }, + "force_entry": { + "description": "Order type for forced entry (e.g., limit, market).", + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + }, + "emergency_exit": { + "description": "Order type for emergency exit (e.g., limit, market).", + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + "default": "market", + }, + "stoploss": { + "description": "Order type for stop loss (e.g., limit, market).", + "type": "string", + "enum": ORDERTYPE_POSSIBILITIES, + }, + "stoploss_on_exchange": { + "description": "Whether to place stop loss on the exchange.", + "type": "boolean", + }, + "stoploss_price_type": { + "description": "Price type for stop loss (e.g., last, mark, index).", + "type": "string", + "enum": STOPLOSS_PRICE_TYPES, + }, + "stoploss_on_exchange_interval": { + "description": "Interval for stop loss on exchange in seconds.", + "type": "number", + }, + "stoploss_on_exchange_limit_ratio": { + "description": "Limit ratio for stop loss on exchange.", + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + }, + }, + "required": ["entry", "exit", "stoploss", "stoploss_on_exchange"], + }, + "order_time_in_force": { + "description": f"Time in force configuration for orders. {__IN_STRATEGY}", + "type": "object", + "properties": { + "entry": { + "description": "Time in force for entry orders.", + "type": "string", + "enum": ORDERTIF_POSSIBILITIES, + }, + "exit": { + "description": "Time in force for exit orders.", + "type": "string", + "enum": ORDERTIF_POSSIBILITIES, + }, + }, + "required": REQUIRED_ORDERTIF, + }, + "coingecko": { + "description": "Configuration for CoinGecko API.", + "type": "object", + "properties": { + "is_demo": { + "description": "Whether to use CoinGecko in demo mode.", + "type": "boolean", + "default": True, + }, + "api_key": {"description": "API key for accessing CoinGecko.", "type": "string"}, + }, + "required": ["is_demo", "api_key"], + }, + "exchange": { + "description": "Exchange configuration.", + "$ref": "#/definitions/exchange", + }, + "edge": { + "description": "Edge configuration.", + "$ref": "#/definitions/edge", + }, + "freqai": { + "description": "FreqAI configuration.", + "$ref": "#/definitions/freqai", + }, + "external_message_consumer": { + "description": "Configuration for external message consumer.", + "$ref": "#/definitions/external_message_consumer", + }, + "experimental": { + "description": "Experimental configuration.", + "type": "object", + "properties": {"block_bad_exchanges": {"type": "boolean"}}, + }, + "pairlists": { + "description": "Configuration for pairlists.", + "type": "array", + "items": { + "type": "object", + "properties": { + "method": { + "description": "Method used for generating the pairlist.", + "type": "string", + "enum": AVAILABLE_PAIRLISTS, + }, + }, + "required": ["method"], + }, + }, + "protections": { + "description": "Configuration for various protections.", + "type": "array", + "items": { + "type": "object", + "properties": { + "method": { + "description": "Method used for the protection.", + "type": "string", + "enum": AVAILABLE_PROTECTIONS, + }, + "stop_duration": { + "description": ( + "Duration to lock the pair after a protection is triggered, " + "in minutes." + ), + "type": "number", + "minimum": 0.0, + }, + "stop_duration_candles": { + "description": ( + "Duration to lock the pair after a protection is triggered, in " + "number of candles." + ), + "type": "number", + "minimum": 0, + }, + "trade_limit": { + "description": "Minimum number of trades required during lookback period.", + "type": "number", + "minimum": 1, + }, + "lookback_period": { + "description": "Period to look back for protection checks, in minutes.", + "type": "number", + "minimum": 1, + }, + "lookback_period_candles": { + "description": ( + "Period to look back for protection checks, in number " "of candles." + ), + "type": "number", + "minimum": 1, + }, + }, + "required": ["method"], + }, + }, + "telegram": { + "description": "Telegram settings.", + "type": "object", + "properties": { + "enabled": { + "description": "Enable Telegram notifications.", + "type": "boolean", + }, + "token": {"description": "Telegram bot token.", "type": "string"}, + "chat_id": { + "description": "Telegram chat ID", + "type": "string", + }, + "allow_custom_messages": { + "description": "Allow sending custom messages from the Strategy.", + "type": "boolean", + "default": True, + }, + "balance_dust_level": { + "description": "Minimum balance level to consider as dust.", + "type": "number", + "minimum": 0.0, + }, + "notification_settings": { + "description": "Settings for different types of notifications.", + "type": "object", + "default": {}, + "properties": { + "status": { + "description": "Telegram setting for status updates.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + "warning": { + "description": "Telegram setting for warnings.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + "startup": { + "description": "Telegram setting for startup messages.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + "entry": { + "description": "Telegram setting for entry signals.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + "entry_fill": { + "description": "Telegram setting for entry fill signals.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "off", + }, + "entry_cancel": { + "description": "Telegram setting for entry cancel signals.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + "exit": { + "description": "Telegram setting for exit signals.", + "type": ["string", "object"], + "additionalProperties": { + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + }, + "exit_fill": { + "description": "Telegram setting for exit fill signals.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "on", + }, + "exit_cancel": { + "description": "Telegram setting for exit cancel signals.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + }, + "protection_trigger": { + "description": "Telegram setting for protection triggers.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "on", + }, + "protection_trigger_global": { + "description": "Telegram setting for global protection triggers.", + "type": "string", + "enum": TELEGRAM_SETTING_OPTIONS, + "default": "on", + }, + }, + }, + "reload": { + "description": "Add Reload button to certain messages.", + "type": "boolean", + }, + }, + "required": ["enabled", "token", "chat_id"], + }, + "webhook": { + "description": "Webhook settings.", + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "url": {"type": "string"}, + "format": {"type": "string", "enum": WEBHOOK_FORMAT_OPTIONS, "default": "form"}, + "retries": {"type": "integer", "minimum": 0}, + "retry_delay": {"type": "number", "minimum": 0}, + **__MESSAGE_TYPE_DICT, + }, + }, + "discord": { + "description": "Discord settings.", + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "webhook_url": {"type": "string"}, + "exit_fill": { + "type": "array", + "items": {"type": "object"}, + "default": [ + {"Trade ID": "{trade_id}"}, + {"Exchange": "{exchange}"}, + {"Pair": "{pair}"}, + {"Direction": "{direction}"}, + {"Open rate": "{open_rate}"}, + {"Close rate": "{close_rate}"}, + {"Amount": "{amount}"}, + {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"}, + {"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"}, + {"Profit": "{profit_amount} {stake_currency}"}, + {"Profitability": "{profit_ratio:.2%}"}, + {"Enter tag": "{enter_tag}"}, + {"Exit Reason": "{exit_reason}"}, + {"Strategy": "{strategy}"}, + {"Timeframe": "{timeframe}"}, + ], + }, + "entry_fill": { + "type": "array", + "items": {"type": "object"}, + "default": [ + {"Trade ID": "{trade_id}"}, + {"Exchange": "{exchange}"}, + {"Pair": "{pair}"}, + {"Direction": "{direction}"}, + {"Open rate": "{open_rate}"}, + {"Amount": "{amount}"}, + {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"}, + {"Enter tag": "{enter_tag}"}, + {"Strategy": "{strategy} {timeframe}"}, + ], + }, + }, + }, + "api_server": { + "description": "API server settings.", + "type": "object", + "properties": { + "enabled": {"description": "Whether the API server is enabled.", "type": "boolean"}, + "listen_ip_address": { + "description": "IP address the API server listens on.", + "format": "ipv4", + }, + "listen_port": { + "description": "Port the API server listens on.", + "type": "integer", + "minimum": 1024, + "maximum": 65535, + }, + "username": { + "description": "Username for API server authentication.", + "type": "string", + }, + "password": { + "description": "Password for API server authentication.", + "type": "string", + }, + "ws_token": { + "description": "WebSocket token for API server.", + "type": ["string", "array"], + "items": {"type": "string"}, + }, + "jwt_secret_key": { + "description": "Secret key for JWT authentication.", + "type": "string", + }, + "CORS_origins": { + "description": "List of allowed CORS origins.", + "type": "array", + "items": {"type": "string"}, + }, + "x": { + "description": "Logging verbosity level.", + "type": "string", + "enum": ["error", "info"], + }, + }, + "required": ["enabled", "listen_ip_address", "listen_port", "username", "password"], + }, + "db_url": { + "description": "Database connection URL.", + "type": "string", + }, + "export": { + "description": "Type of data to export.", + "type": "string", + "enum": EXPORT_OPTIONS, + "default": "trades", + }, + "disableparamexport": { + "description": "Disable parameter export.", + "type": "boolean", + }, + "initial_state": { + "description": "Initial state of the system.", + "type": "string", + "enum": ["running", "stopped"], + }, + "force_entry_enable": { + "description": "Force enable entry.", + "type": "boolean", + }, + "disable_dataframe_checks": { + "description": "Disable checks on dataframes.", + "type": "boolean", + }, + "internals": { + "description": "Internal settings.", + "type": "object", + "default": {}, + "properties": { + "process_throttle_secs": { + "description": "Throttle time in seconds for processing.", + "type": "integer", + }, + "interval": { + "description": "Interval time in seconds.", + "type": "integer", + }, + "sd_notify": { + "description": "Enable systemd notify.", + "type": "boolean", + }, + }, + }, + "dataformat_ohlcv": { + "description": "Data format for OHLCV data.", + "type": "string", + "enum": AVAILABLE_DATAHANDLERS, + "default": "feather", + }, + "dataformat_trades": { + "description": "Data format for trade data.", + "type": "string", + "enum": AVAILABLE_DATAHANDLERS, + "default": "feather", + }, + "position_adjustment_enable": { + "description": f"Enable position adjustment. {__IN_STRATEGY}", + "type": "boolean", + }, + "max_entry_position_adjustment": { + "description": f"Maximum entry position adjustment allowed. {__IN_STRATEGY}", + "type": ["integer", "number"], + "minimum": -1, + }, + "orderflow": { + "description": "Settings related to order flow.", + "type": "object", + "properties": { + "cache_size": { + "description": "Size of the cache for order flow data.", + "type": "number", + "minimum": 1, + "default": 1500, + }, + "max_candles": { + "description": "Maximum number of candles to consider.", + "type": "number", + "minimum": 1, + "default": 1500, + }, + "scale": { + "description": "Scale factor for order flow data.", + "type": "number", + "minimum": 0.0, + }, + "stacked_imbalance_range": { + "description": "Range for stacked imbalance.", + "type": "number", + "minimum": 0, + }, + "imbalance_volume": { + "description": "Volume threshold for imbalance.", + "type": "number", + "minimum": 0, + }, + "imbalance_ratio": { + "description": "Ratio threshold for imbalance.", + "type": "number", + "minimum": 0.0, + }, + }, + "required": [ + "max_candles", + "scale", + "stacked_imbalance_range", + "imbalance_volume", + "imbalance_ratio", + ], + }, + }, + "definitions": { + "exchange": { + "description": "Exchange configuration settings.", + "type": "object", + "properties": { + "name": {"description": "Name of the exchange.", "type": "string"}, + "enable_ws": { + "description": "Enable WebSocket connections to the exchange.", + "type": "boolean", + "default": True, + }, + "key": { + "description": "API key for the exchange.", + "type": "string", + "default": "", + }, + "secret": { + "description": "API secret for the exchange.", + "type": "string", + "default": "", + }, + "password": { + "description": "Password for the exchange, if required.", + "type": "string", + "default": "", + }, + "uid": {"description": "User ID for the exchange, if required.", "type": "string"}, + "pair_whitelist": { + "description": "List of whitelisted trading pairs.", + "type": "array", + "items": {"type": "string"}, + "uniqueItems": True, + }, + "pair_blacklist": { + "description": "List of blacklisted trading pairs.", + "type": "array", + "items": {"type": "string"}, + "uniqueItems": True, + }, + "unknown_fee_rate": { + "description": "Fee rate for unknown markets.", + "type": "number", + }, + "outdated_offset": { + "description": "Offset for outdated data in minutes.", + "type": "integer", + "minimum": 1, + }, + "markets_refresh_interval": { + "description": "Interval for refreshing market data in minutes.", + "type": "integer", + "default": 60, + }, + "ccxt_config": {"description": "CCXT configuration settings.", "type": "object"}, + "ccxt_async_config": { + "description": "CCXT asynchronous configuration settings.", + "type": "object", + }, + }, + "required": ["name"], + }, + "edge": { + "type": "object", + "properties": { + "enabled": {"type": "boolean"}, + "process_throttle_secs": {"type": "integer", "minimum": 600}, + "calculate_since_number_of_days": {"type": "integer"}, + "allowed_risk": {"type": "number"}, + "stoploss_range_min": {"type": "number"}, + "stoploss_range_max": {"type": "number"}, + "stoploss_range_step": {"type": "number"}, + "minimum_winrate": {"type": "number"}, + "minimum_expectancy": {"type": "number"}, + "min_trade_number": {"type": "number"}, + "max_trade_duration_minute": {"type": "integer"}, + "remove_pumps": {"type": "boolean"}, + }, + "required": ["process_throttle_secs", "allowed_risk"], + }, + "external_message_consumer": { + "description": "Configuration for external message consumer.", + "type": "object", + "properties": { + "enabled": { + "description": "Whether the external message consumer is enabled.", + "type": "boolean", + "default": False, + }, + "producers": { + "description": "List of producers for the external message consumer.", + "type": "array", + "items": { + "type": "object", + "properties": { + "name": { + "description": "Name of the producer.", + "type": "string", + }, + "host": { + "description": "Host of the producer.", + "type": "string", + }, + "port": { + "description": "Port of the producer.", + "type": "integer", + "default": 8080, + "minimum": 0, + "maximum": 65535, + }, + "secure": { + "description": "Whether to use SSL to connect to the producer.", + "type": "boolean", + "default": False, + }, + "ws_token": { + "description": "WebSocket token for the producer.", + "type": "string", + }, + }, + "required": ["name", "host", "ws_token"], + }, + }, + "wait_timeout": { + "description": "Wait timeout in seconds.", + "type": "integer", + "minimum": 0, + }, + "sleep_time": { + "description": "Sleep time in seconds before retrying to connect.", + "type": "integer", + "minimum": 0, + }, + "ping_timeout": { + "description": "Ping timeout in seconds.", + "type": "integer", + "minimum": 0, + }, + "remove_entry_exit_signals": { + "description": "Remove signal columns from the dataframe (set them to 0)", + "type": "boolean", + "default": False, + }, + "initial_candle_limit": { + "description": "Initial candle limit.", + "type": "integer", + "minimum": 0, + "maximum": 1500, + "default": 1500, + }, + "message_size_limit": { + "description": "Message size limit in megabytes.", + "type": "integer", + "minimum": 1, + "maximum": 20, + "default": 8, + }, + }, + "required": ["producers"], + }, + "freqai": { + "type": "object", + "properties": { + "enabled": { + "description": "Whether freqAI is enabled.", + "type": "boolean", + "default": False, + }, + "keras": { + "description": "Use Keras for model training.", + "type": "boolean", + "default": False, + }, + "write_metrics_to_disk": { + "description": "Write metrics to disk?", + "type": "boolean", + "default": False, + }, + "purge_old_models": { + "description": "Number of models to keep on disk.", + "type": ["boolean", "number"], + "default": 2, + }, + "conv_width": { + "description": "The width of a neural network input tensor.", + "type": "integer", + "default": 1, + }, + "train_period_days": { + "description": ( + "Number of days to use for the training data (width of the sliding window)" + ), + "type": "integer", + "default": 0, + }, + "backtest_period_days": { + "description": ( + "Number of days to inference from the trained model before sliding the " + "`train_period_days` window " + ), + "type": "number", + "default": 7, + }, + "identifier": { + "description": ( + "A unique ID for the current model. " + "Must be changed when modifying features." + ), + "type": "string", + "default": "example", + }, + "feature_parameters": { + "description": "The parameters used to engineer the feature set", + "type": "object", + "properties": { + "include_corr_pairlist": { + "description": "List of correlated pairs to include in the features.", + "type": "array", + }, + "include_timeframes": { + "description": ( + "A list of timeframes that all indicators in " + "`feature_engineering_expand_*()` will be created for." + ), + "type": "array", + }, + "label_period_candles": { + "description": ( + "Number of candles into the future to use for labeling the period." + "This can be used in `set_freqai_targets()`." + ), + "type": "integer", + }, + "include_shifted_candles": { + "description": ( + "Add features from previous candles to subsequent candles with " + "the intent of adding historical information." + ), + "type": "integer", + "default": 0, + }, + "DI_threshold": { + "description": ( + "Activates the use of the Dissimilarity Index for " + "outlier detection when set to > 0." + ), + "type": "number", + "default": 0, + }, + "weight_factor": { + "description": ( + "Weight training data points according to their recency." + ), + "type": "number", + "default": 0, + }, + "principal_component_analysis": { + "description": ( + "Automatically reduce the dimensionality of the data set using " + "Principal Component Analysis" + ), + "type": "boolean", + "default": False, + }, + "use_SVM_to_remove_outliers": { + "description": "Use SVM to remove outliers from the features.", + "type": "boolean", + "default": False, + }, + "plot_feature_importances": { + "description": "Create feature importance plots for each model.", + "type": "integer", + "default": 0, + }, + "svm_params": { + "description": ( + "All parameters available in Sklearn's `SGDOneClassSVM()`." + ), + "type": "object", + "properties": { + "shuffle": { + "description": "Whether to shuffle data before applying SVM.", + "type": "boolean", + "default": False, + }, + "nu": { + "type": "number", + "default": 0.1, + }, + }, + }, + "shuffle_after_split": { + "description": ( + "Split the data into train and test sets, and then shuffle " + "both sets individually." + ), + "type": "boolean", + "default": False, + }, + "buffer_train_data_candles": { + "description": ( + "Cut `buffer_train_data_candles` off the beginning and end of the " + "training data *after* the indicators were populated." + ), + "type": "integer", + "default": 0, + }, + }, + "required": [ + "include_timeframes", + "include_corr_pairlist", + ], + }, + "data_split_parameters": { + "descriptions": ( + "Additional parameters for scikit-learn's test_train_split() function." + ), + "type": "object", + "properties": { + "test_size": {"type": "number"}, + "random_state": {"type": "integer"}, + "shuffle": {"type": "boolean", "default": False}, + }, + }, + "model_training_parameters": { + "description": ( + "Flexible dictionary that includes all parameters available by " + "the selected model library. " + ), + "type": "object", + }, + "rl_config": { + "type": "object", + "properties": { + "drop_ohlc_from_features": { + "description": ( + "Do not include the normalized ohlc data in the feature set." + ), + "type": "boolean", + "default": False, + }, + "train_cycles": { + "description": "Number of training cycles to perform.", + "type": "integer", + }, + "max_trade_duration_candles": { + "description": ( + "Guides the agent training to keep trades below desired length." + ), + "type": "integer", + }, + "add_state_info": { + "description": ( + "Include state information in the feature set for " + "training and inference." + ), + "type": "boolean", + "default": False, + }, + "max_training_drawdown_pct": { + "description": "Maximum allowed drawdown percentage during training.", + "type": "number", + "default": 0.02, + }, + "cpu_count": { + "description": "Number of threads/CPU's to use for training.", + "type": "integer", + "default": 1, + }, + "model_type": { + "description": "Model string from stable_baselines3 or SBcontrib.", + "type": "string", + "default": "PPO", + }, + "policy_type": { + "description": ( + "One of the available policy types from stable_baselines3." + ), + "type": "string", + "default": "MlpPolicy", + }, + "net_arch": { + "description": "Architecture of the neural network.", + "type": "array", + "default": [128, 128], + }, + "randomize_starting_position": { + "description": ( + "Randomize the starting point of each episode to avoid overfitting." + ), + "type": "boolean", + "default": False, + }, + "progress_bar": { + "description": "Display a progress bar with the current progress.", + "type": "boolean", + "default": True, + }, + "model_reward_parameters": { + "description": "Parameters for configuring the reward model.", + "type": "object", + "properties": { + "rr": { + "type": "number", + "default": 1, + "description": "Reward ratio parameter.", + }, + "profit_aim": { + "type": "number", + "default": 0.025, + "description": "Profit aim parameter.", + }, + }, + }, + }, + }, + }, + "required": [ + "enabled", + "train_period_days", + "backtest_period_days", + "identifier", + "feature_parameters", + "data_split_parameters", + ], + }, + }, +} + +SCHEMA_TRADE_REQUIRED = [ + "exchange", + "timeframe", + "max_open_trades", + "stake_currency", + "stake_amount", + "tradable_balance_ratio", + "last_stake_amount_min_ratio", + "dry_run", + "dry_run_wallet", + "exit_pricing", + "entry_pricing", + "stoploss", + "minimal_roi", + "internals", + "dataformat_ohlcv", + "dataformat_trades", +] + +SCHEMA_BACKTEST_REQUIRED = [ + "exchange", + "stake_currency", + "stake_amount", + "dry_run_wallet", + "dataformat_ohlcv", + "dataformat_trades", +] +SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [ + "stoploss", + "minimal_roi", + "max_open_trades", +] + +SCHEMA_MINIMAL_REQUIRED = [ + "exchange", + "dry_run", + "dataformat_ohlcv", + "dataformat_trades", +] +SCHEMA_MINIMAL_WEBSERVER = SCHEMA_MINIMAL_REQUIRED + [ + "api_server", +] diff --git a/freqtrade/configuration/config_secrets.py b/freqtrade/configuration/config_secrets.py index 5a694dddf..427e09088 100644 --- a/freqtrade/configuration/config_secrets.py +++ b/freqtrade/configuration/config_secrets.py @@ -14,9 +14,13 @@ def sanitize_config(config: Config, *, show_sensitive: bool = False) -> Config: return config keys_to_remove = [ "exchange.key", + "exchange.apiKey", "exchange.secret", "exchange.password", "exchange.uid", + "exchange.accountId", + "exchange.walletAddress", + "exchange.privateKey", "telegram.token", "telegram.chat_id", "discord.webhook_url", diff --git a/freqtrade/configuration/config_validation.py b/freqtrade/configuration/config_validation.py index 3f8e5c9ef..f7f021b9c 100644 --- a/freqtrade/configuration/config_validation.py +++ b/freqtrade/configuration/config_validation.py @@ -6,8 +6,16 @@ from typing import Any, Dict from jsonschema import Draft4Validator, validators from jsonschema.exceptions import ValidationError, best_match -from freqtrade import constants +from freqtrade.configuration.config_schema import ( + CONF_SCHEMA, + SCHEMA_BACKTEST_REQUIRED, + SCHEMA_BACKTEST_REQUIRED_FINAL, + SCHEMA_MINIMAL_REQUIRED, + SCHEMA_MINIMAL_WEBSERVER, + SCHEMA_TRADE_REQUIRED, +) from freqtrade.configuration.deprecated_settings import process_deprecated_setting +from freqtrade.constants import UNLIMITED_STAKE_AMOUNT from freqtrade.enums import RunMode, TradingMode from freqtrade.exceptions import ConfigurationError @@ -41,18 +49,18 @@ def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> D :param conf: Config in JSON format :return: Returns the config if valid, otherwise throw an exception """ - conf_schema = deepcopy(constants.CONF_SCHEMA) + conf_schema = deepcopy(CONF_SCHEMA) if conf.get("runmode", RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE): - conf_schema["required"] = constants.SCHEMA_TRADE_REQUIRED + conf_schema["required"] = SCHEMA_TRADE_REQUIRED elif conf.get("runmode", RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT): if preliminary: - conf_schema["required"] = constants.SCHEMA_BACKTEST_REQUIRED + conf_schema["required"] = SCHEMA_BACKTEST_REQUIRED else: - conf_schema["required"] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL + conf_schema["required"] = SCHEMA_BACKTEST_REQUIRED_FINAL elif conf.get("runmode", RunMode.OTHER) == RunMode.WEBSERVER: - conf_schema["required"] = constants.SCHEMA_MINIMAL_WEBSERVER + conf_schema["required"] = SCHEMA_MINIMAL_WEBSERVER else: - conf_schema["required"] = constants.SCHEMA_MINIMAL_REQUIRED + conf_schema["required"] = SCHEMA_MINIMAL_REQUIRED try: FreqtradeValidator(conf_schema).validate(conf) return conf @@ -83,6 +91,7 @@ def validate_config_consistency(conf: Dict[str, Any], *, preliminary: bool = Fal _validate_freqai_include_timeframes(conf, preliminary=preliminary) _validate_consumers(conf) validate_migrated_strategy_settings(conf) + _validate_orderflow(conf) # validate configuration before returning logger.info("Validating configuration ...") @@ -97,7 +106,7 @@ def _validate_unlimited_amount(conf: Dict[str, Any]) -> None: if ( not conf.get("edge", {}).get("enabled") and conf.get("max_open_trades") == float("inf") - and conf.get("stake_amount") == constants.UNLIMITED_STAKE_AMOUNT + and conf.get("stake_amount") == UNLIMITED_STAKE_AMOUNT ): raise ConfigurationError("`max_open_trades` and `stake_amount` cannot both be unlimited.") @@ -421,6 +430,14 @@ def _validate_consumers(conf: Dict[str, Any]) -> None: ) +def _validate_orderflow(conf: Dict[str, Any]) -> None: + if conf.get("exchange", {}).get("use_public_trades"): + if "orderflow" not in conf: + raise ConfigurationError( + "Orderflow is a required configuration key when using public trades." + ) + + def _strategy_settings(conf: Dict[str, Any]) -> None: process_deprecated_setting(conf, None, "use_sell_signal", None, "use_exit_signal") process_deprecated_setting(conf, None, "sell_profit_only", None, "exit_profit_only") diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 17a829955..120f463f3 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -4,9 +4,9 @@ bot constants """ -from typing import Any, Dict, List, Literal, Tuple +from typing import Any, Dict, List, Literal, Optional, Tuple -from freqtrade.enums import CandleType, PriceType, RPCMessageType +from freqtrade.enums import CandleType, PriceType DOCS_LINK = "https://www.freqtrade.io/en/stable" @@ -69,6 +69,7 @@ DEFAULT_DATAFRAME_COLUMNS = ["date", "open", "high", "low", "close", "volume"] # Don't modify sequence of DEFAULT_TRADES_COLUMNS # it has wide consequences for stored trades files DEFAULT_TRADES_COLUMNS = ["timestamp", "id", "type", "side", "price", "amount", "cost"] +DEFAULT_ORDERFLOW_COLUMNS = ["level", "bid", "ask", "delta"] TRADES_DTYPES = { "timestamp": "int64", "id": "str", @@ -172,586 +173,6 @@ MINIMAL_CONFIG = { }, } -__MESSAGE_TYPE_DICT: Dict[str, Dict[str, str]] = {x: {"type": "object"} for x in RPCMessageType} - -# Required json-schema for user specified config -CONF_SCHEMA = { - "type": "object", - "properties": { - "max_open_trades": {"type": ["integer", "number"], "minimum": -1}, - "new_pairs_days": {"type": "integer", "default": 30}, - "timeframe": {"type": "string"}, - "stake_currency": {"type": "string"}, - "stake_amount": { - "type": ["number", "string"], - "minimum": 0.0001, - "pattern": UNLIMITED_STAKE_AMOUNT, - }, - "tradable_balance_ratio": {"type": "number", "minimum": 0.0, "maximum": 1, "default": 0.99}, - "available_capital": { - "type": "number", - "minimum": 0, - }, - "amend_last_stake_amount": {"type": "boolean", "default": False}, - "last_stake_amount_min_ratio": { - "type": "number", - "minimum": 0.0, - "maximum": 1.0, - "default": 0.5, - }, - "fiat_display_currency": {"type": "string", "enum": SUPPORTED_FIAT}, - "dry_run": {"type": "boolean"}, - "dry_run_wallet": {"type": "number", "default": DRY_RUN_WALLET}, - "cancel_open_orders_on_exit": {"type": "boolean", "default": False}, - "process_only_new_candles": {"type": "boolean"}, - "minimal_roi": { - "type": "object", - "patternProperties": {"^[0-9.]+$": {"type": "number"}}, - }, - "amount_reserve_percent": {"type": "number", "minimum": 0.0, "maximum": 0.5}, - "stoploss": {"type": "number", "maximum": 0, "exclusiveMaximum": True}, - "trailing_stop": {"type": "boolean"}, - "trailing_stop_positive": {"type": "number", "minimum": 0, "maximum": 1}, - "trailing_stop_positive_offset": {"type": "number", "minimum": 0, "maximum": 1}, - "trailing_only_offset_is_reached": {"type": "boolean"}, - "use_exit_signal": {"type": "boolean"}, - "exit_profit_only": {"type": "boolean"}, - "exit_profit_offset": {"type": "number"}, - "fee": {"type": "number", "minimum": 0, "maximum": 0.1}, - "ignore_roi_if_entry_signal": {"type": "boolean"}, - "ignore_buying_expired_candle_after": {"type": "number"}, - "trading_mode": {"type": "string", "enum": TRADING_MODES}, - "margin_mode": {"type": "string", "enum": MARGIN_MODES}, - "reduce_df_footprint": {"type": "boolean", "default": False}, - "minimum_trade_amount": {"type": "number", "default": 10}, - "targeted_trade_amount": {"type": "number", "default": 20}, - "lookahead_analysis_exportfilename": {"type": "string"}, - "startup_candle": { - "type": "array", - "uniqueItems": True, - "default": [199, 399, 499, 999, 1999], - }, - "liquidation_buffer": {"type": "number", "minimum": 0.0, "maximum": 0.99}, - "backtest_breakdown": { - "type": "array", - "items": {"type": "string", "enum": BACKTEST_BREAKDOWNS}, - }, - "bot_name": {"type": "string"}, - "unfilledtimeout": { - "type": "object", - "properties": { - "entry": {"type": "number", "minimum": 1}, - "exit": {"type": "number", "minimum": 1}, - "exit_timeout_count": {"type": "number", "minimum": 0, "default": 0}, - "unit": {"type": "string", "enum": TIMEOUT_UNITS, "default": "minutes"}, - }, - }, - "entry_pricing": { - "type": "object", - "properties": { - "price_last_balance": { - "type": "number", - "minimum": 0, - "maximum": 1, - "exclusiveMaximum": False, - }, - "price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"}, - "use_order_book": {"type": "boolean"}, - "order_book_top": { - "type": "integer", - "minimum": 1, - "maximum": 50, - }, - "check_depth_of_market": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "bids_to_ask_delta": {"type": "number", "minimum": 0}, - }, - }, - }, - "required": ["price_side"], - }, - "exit_pricing": { - "type": "object", - "properties": { - "price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"}, - "price_last_balance": { - "type": "number", - "minimum": 0, - "maximum": 1, - "exclusiveMaximum": False, - }, - "use_order_book": {"type": "boolean"}, - "order_book_top": { - "type": "integer", - "minimum": 1, - "maximum": 50, - }, - }, - "required": ["price_side"], - }, - "custom_price_max_distance_ratio": {"type": "number", "minimum": 0.0}, - "order_types": { - "type": "object", - "properties": { - "entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "force_exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "force_entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "emergency_exit": { - "type": "string", - "enum": ORDERTYPE_POSSIBILITIES, - "default": "market", - }, - "stoploss": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES}, - "stoploss_on_exchange": {"type": "boolean"}, - "stoploss_price_type": {"type": "string", "enum": STOPLOSS_PRICE_TYPES}, - "stoploss_on_exchange_interval": {"type": "number"}, - "stoploss_on_exchange_limit_ratio": { - "type": "number", - "minimum": 0.0, - "maximum": 1.0, - }, - }, - "required": ["entry", "exit", "stoploss", "stoploss_on_exchange"], - }, - "order_time_in_force": { - "type": "object", - "properties": { - "entry": {"type": "string", "enum": ORDERTIF_POSSIBILITIES}, - "exit": {"type": "string", "enum": ORDERTIF_POSSIBILITIES}, - }, - "required": REQUIRED_ORDERTIF, - }, - "coingecko": { - "type": "object", - "properties": { - "is_demo": {"type": "boolean", "default": True}, - "api_key": {"type": "string"}, - }, - "required": ["is_demo", "api_key"], - }, - "exchange": {"$ref": "#/definitions/exchange"}, - "edge": {"$ref": "#/definitions/edge"}, - "freqai": {"$ref": "#/definitions/freqai"}, - "external_message_consumer": {"$ref": "#/definitions/external_message_consumer"}, - "experimental": { - "type": "object", - "properties": {"block_bad_exchanges": {"type": "boolean"}}, - }, - "pairlists": { - "type": "array", - "items": { - "type": "object", - "properties": { - "method": {"type": "string", "enum": AVAILABLE_PAIRLISTS}, - }, - "required": ["method"], - }, - }, - "protections": { - "type": "array", - "items": { - "type": "object", - "properties": { - "method": {"type": "string", "enum": AVAILABLE_PROTECTIONS}, - "stop_duration": {"type": "number", "minimum": 0.0}, - "stop_duration_candles": {"type": "number", "minimum": 0}, - "trade_limit": {"type": "number", "minimum": 1}, - "lookback_period": {"type": "number", "minimum": 1}, - "lookback_period_candles": {"type": "number", "minimum": 1}, - }, - "required": ["method"], - }, - }, - "telegram": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "token": {"type": "string"}, - "chat_id": {"type": "string"}, - "allow_custom_messages": {"type": "boolean", "default": True}, - "balance_dust_level": {"type": "number", "minimum": 0.0}, - "notification_settings": { - "type": "object", - "default": {}, - "properties": { - "status": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "warning": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "startup": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "entry": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "entry_fill": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "off", - }, - "entry_cancel": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - }, - "exit": { - "type": ["string", "object"], - "additionalProperties": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - }, - }, - "exit_fill": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "on", - }, - "exit_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS}, - "protection_trigger": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "on", - }, - "protection_trigger_global": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "on", - }, - "show_candle": { - "type": "string", - "enum": ["off", "ohlc"], - "default": "off", - }, - "strategy_msg": { - "type": "string", - "enum": TELEGRAM_SETTING_OPTIONS, - "default": "on", - }, - }, - }, - "reload": {"type": "boolean"}, - }, - "required": ["enabled", "token", "chat_id"], - }, - "webhook": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "url": {"type": "string"}, - "format": {"type": "string", "enum": WEBHOOK_FORMAT_OPTIONS, "default": "form"}, - "retries": {"type": "integer", "minimum": 0}, - "retry_delay": {"type": "number", "minimum": 0}, - **__MESSAGE_TYPE_DICT, - # **{x: {'type': 'object'} for x in RPCMessageType}, - # Below -> Deprecated - "webhookentry": {"type": "object"}, - "webhookentrycancel": {"type": "object"}, - "webhookentryfill": {"type": "object"}, - "webhookexit": {"type": "object"}, - "webhookexitcancel": {"type": "object"}, - "webhookexitfill": {"type": "object"}, - "webhookstatus": {"type": "object"}, - }, - }, - "discord": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "webhook_url": {"type": "string"}, - "exit_fill": { - "type": "array", - "items": {"type": "object"}, - "default": [ - {"Trade ID": "{trade_id}"}, - {"Exchange": "{exchange}"}, - {"Pair": "{pair}"}, - {"Direction": "{direction}"}, - {"Open rate": "{open_rate}"}, - {"Close rate": "{close_rate}"}, - {"Amount": "{amount}"}, - {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"}, - {"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"}, - {"Profit": "{profit_amount} {stake_currency}"}, - {"Profitability": "{profit_ratio:.2%}"}, - {"Enter tag": "{enter_tag}"}, - {"Exit Reason": "{exit_reason}"}, - {"Strategy": "{strategy}"}, - {"Timeframe": "{timeframe}"}, - ], - }, - "entry_fill": { - "type": "array", - "items": {"type": "object"}, - "default": [ - {"Trade ID": "{trade_id}"}, - {"Exchange": "{exchange}"}, - {"Pair": "{pair}"}, - {"Direction": "{direction}"}, - {"Open rate": "{open_rate}"}, - {"Amount": "{amount}"}, - {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"}, - {"Enter tag": "{enter_tag}"}, - {"Strategy": "{strategy} {timeframe}"}, - ], - }, - }, - }, - "api_server": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "listen_ip_address": {"format": "ipv4"}, - "listen_port": {"type": "integer", "minimum": 1024, "maximum": 65535}, - "username": {"type": "string"}, - "password": {"type": "string"}, - "ws_token": {"type": ["string", "array"], "items": {"type": "string"}}, - "jwt_secret_key": {"type": "string"}, - "CORS_origins": {"type": "array", "items": {"type": "string"}}, - "verbosity": {"type": "string", "enum": ["error", "info"]}, - }, - "required": ["enabled", "listen_ip_address", "listen_port", "username", "password"], - }, - "db_url": {"type": "string"}, - "export": {"type": "string", "enum": EXPORT_OPTIONS, "default": "trades"}, - "disableparamexport": {"type": "boolean"}, - "initial_state": {"type": "string", "enum": ["running", "stopped"]}, - "force_entry_enable": {"type": "boolean"}, - "disable_dataframe_checks": {"type": "boolean"}, - "internals": { - "type": "object", - "default": {}, - "properties": { - "process_throttle_secs": {"type": "integer"}, - "interval": {"type": "integer"}, - "sd_notify": {"type": "boolean"}, - }, - }, - "dataformat_ohlcv": { - "type": "string", - "enum": AVAILABLE_DATAHANDLERS, - "default": "feather", - }, - "dataformat_trades": { - "type": "string", - "enum": AVAILABLE_DATAHANDLERS, - "default": "feather", - }, - "position_adjustment_enable": {"type": "boolean"}, - "max_entry_position_adjustment": {"type": ["integer", "number"], "minimum": -1}, - }, - "definitions": { - "exchange": { - "type": "object", - "properties": { - "name": {"type": "string"}, - "enable_ws": {"type": "boolean", "default": True}, - "key": {"type": "string", "default": ""}, - "secret": {"type": "string", "default": ""}, - "password": {"type": "string", "default": ""}, - "uid": {"type": "string"}, - "pair_whitelist": { - "type": "array", - "items": { - "type": "string", - }, - "uniqueItems": True, - }, - "pair_blacklist": { - "type": "array", - "items": { - "type": "string", - }, - "uniqueItems": True, - }, - "unknown_fee_rate": {"type": "number"}, - "outdated_offset": {"type": "integer", "minimum": 1}, - "markets_refresh_interval": {"type": "integer"}, - "ccxt_config": {"type": "object"}, - "ccxt_async_config": {"type": "object"}, - }, - "required": ["name"], - }, - "edge": { - "type": "object", - "properties": { - "enabled": {"type": "boolean"}, - "process_throttle_secs": {"type": "integer", "minimum": 600}, - "calculate_since_number_of_days": {"type": "integer"}, - "allowed_risk": {"type": "number"}, - "stoploss_range_min": {"type": "number"}, - "stoploss_range_max": {"type": "number"}, - "stoploss_range_step": {"type": "number"}, - "minimum_winrate": {"type": "number"}, - "minimum_expectancy": {"type": "number"}, - "min_trade_number": {"type": "number"}, - "max_trade_duration_minute": {"type": "integer"}, - "remove_pumps": {"type": "boolean"}, - }, - "required": ["process_throttle_secs", "allowed_risk"], - }, - "external_message_consumer": { - "type": "object", - "properties": { - "enabled": {"type": "boolean", "default": False}, - "producers": { - "type": "array", - "items": { - "type": "object", - "properties": { - "name": {"type": "string"}, - "host": {"type": "string"}, - "port": { - "type": "integer", - "default": 8080, - "minimum": 0, - "maximum": 65535, - }, - "secure": {"type": "boolean", "default": False}, - "ws_token": {"type": "string"}, - }, - "required": ["name", "host", "ws_token"], - }, - }, - "wait_timeout": {"type": "integer", "minimum": 0}, - "sleep_time": {"type": "integer", "minimum": 0}, - "ping_timeout": {"type": "integer", "minimum": 0}, - "remove_entry_exit_signals": {"type": "boolean", "default": False}, - "initial_candle_limit": { - "type": "integer", - "minimum": 0, - "maximum": 1500, - "default": 1500, - }, - "message_size_limit": { # In megabytes - "type": "integer", - "minimum": 1, - "maximum": 20, - "default": 8, - }, - }, - "required": ["producers"], - }, - "freqai": { - "type": "object", - "properties": { - "enabled": {"type": "boolean", "default": False}, - "keras": {"type": "boolean", "default": False}, - "write_metrics_to_disk": {"type": "boolean", "default": False}, - "purge_old_models": {"type": ["boolean", "number"], "default": 2}, - "conv_width": {"type": "integer", "default": 1}, - "train_period_days": {"type": "integer", "default": 0}, - "backtest_period_days": {"type": "number", "default": 7}, - "identifier": {"type": "string", "default": "example"}, - "feature_parameters": { - "type": "object", - "properties": { - "include_corr_pairlist": {"type": "array"}, - "include_timeframes": {"type": "array"}, - "label_period_candles": {"type": "integer"}, - "include_shifted_candles": {"type": "integer", "default": 0}, - "DI_threshold": {"type": "number", "default": 0}, - "weight_factor": {"type": "number", "default": 0}, - "principal_component_analysis": {"type": "boolean", "default": False}, - "use_SVM_to_remove_outliers": {"type": "boolean", "default": False}, - "plot_feature_importances": {"type": "integer", "default": 0}, - "svm_params": { - "type": "object", - "properties": { - "shuffle": {"type": "boolean", "default": False}, - "nu": {"type": "number", "default": 0.1}, - }, - }, - "shuffle_after_split": {"type": "boolean", "default": False}, - "buffer_train_data_candles": {"type": "integer", "default": 0}, - }, - "required": [ - "include_timeframes", - "include_corr_pairlist", - ], - }, - "data_split_parameters": { - "type": "object", - "properties": { - "test_size": {"type": "number"}, - "random_state": {"type": "integer"}, - "shuffle": {"type": "boolean", "default": False}, - }, - }, - "model_training_parameters": {"type": "object"}, - "rl_config": { - "type": "object", - "properties": { - "drop_ohlc_from_features": {"type": "boolean", "default": False}, - "train_cycles": {"type": "integer"}, - "max_trade_duration_candles": {"type": "integer"}, - "add_state_info": {"type": "boolean", "default": False}, - "max_training_drawdown_pct": {"type": "number", "default": 0.02}, - "cpu_count": {"type": "integer", "default": 1}, - "model_type": {"type": "string", "default": "PPO"}, - "policy_type": {"type": "string", "default": "MlpPolicy"}, - "net_arch": {"type": "array", "default": [128, 128]}, - "randomize_starting_position": {"type": "boolean", "default": False}, - "progress_bar": {"type": "boolean", "default": True}, - "model_reward_parameters": { - "type": "object", - "properties": { - "rr": {"type": "number", "default": 1}, - "profit_aim": {"type": "number", "default": 0.025}, - }, - }, - }, - }, - }, - "required": [ - "enabled", - "train_period_days", - "backtest_period_days", - "identifier", - "feature_parameters", - "data_split_parameters", - ], - }, - }, -} - -SCHEMA_TRADE_REQUIRED = [ - "exchange", - "timeframe", - "max_open_trades", - "stake_currency", - "stake_amount", - "tradable_balance_ratio", - "last_stake_amount_min_ratio", - "dry_run", - "dry_run_wallet", - "exit_pricing", - "entry_pricing", - "stoploss", - "minimal_roi", - "internals", - "dataformat_ohlcv", - "dataformat_trades", -] - -SCHEMA_BACKTEST_REQUIRED = [ - "exchange", - "stake_currency", - "stake_amount", - "dry_run_wallet", - "dataformat_ohlcv", - "dataformat_trades", -] -SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [ - "stoploss", - "minimal_roi", - "max_open_trades", -] - -SCHEMA_MINIMAL_REQUIRED = [ - "exchange", - "dry_run", - "dataformat_ohlcv", - "dataformat_trades", -] -SCHEMA_MINIMAL_WEBSERVER = SCHEMA_MINIMAL_REQUIRED + [ - "api_server", -] CANCEL_REASON = { "TIMEOUT": "cancelled due to timeout", @@ -772,6 +193,9 @@ ListPairsWithTimeframes = List[PairWithTimeframe] # Type for trades list TradeList = List[List] +# ticks, pair, timeframe, CandleType +TickWithTimeframe = Tuple[str, str, CandleType, Optional[int], Optional[int]] +ListTicksWithTimeframes = List[TickWithTimeframe] LongShort = Literal["long", "short"] EntryExit = Literal["entry", "exit"] diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py index eef415879..2895b4181 100644 --- a/freqtrade/data/btanalysis.py +++ b/freqtrade/data/btanalysis.py @@ -185,7 +185,7 @@ def load_and_merge_backtest_result(strategy_name: str, filename: Path, results: """ bt_data = load_backtest_stats(filename) k: Literal["metadata", "strategy"] - for k in ("metadata", "strategy"): # type: ignore + for k in ("metadata", "strategy"): results[k][strategy_name] = bt_data[k][strategy_name] results["metadata"][strategy_name]["filename"] = filename.stem comparison = bt_data["strategy_comparison"] diff --git a/freqtrade/data/converter/__init__.py b/freqtrade/data/converter/__init__.py index 76c8a7edc..5966d23b4 100644 --- a/freqtrade/data/converter/__init__.py +++ b/freqtrade/data/converter/__init__.py @@ -8,6 +8,7 @@ from freqtrade.data.converter.converter import ( trim_dataframe, trim_dataframes, ) +from freqtrade.data.converter.orderflow import populate_dataframe_with_trades from freqtrade.data.converter.trade_converter import ( convert_trades_format, convert_trades_to_ohlcv, @@ -30,6 +31,7 @@ __all__ = [ "trim_dataframes", "convert_trades_format", "convert_trades_to_ohlcv", + "populate_dataframe_with_trades", "trades_convert_types", "trades_df_remove_duplicates", "trades_dict_to_list", diff --git a/freqtrade/data/converter/orderflow.py b/freqtrade/data/converter/orderflow.py new file mode 100644 index 000000000..11d17e52f --- /dev/null +++ b/freqtrade/data/converter/orderflow.py @@ -0,0 +1,295 @@ +""" +Functions to convert orderflow data from public_trades +""" + +import logging +import time +import typing +from collections import OrderedDict +from datetime import datetime +from typing import Tuple + +import numpy as np +import pandas as pd + +from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS +from freqtrade.enums import RunMode +from freqtrade.exceptions import DependencyException + + +logger = logging.getLogger(__name__) + + +def _init_dataframe_with_trades_columns(dataframe: pd.DataFrame): + """ + Populates a dataframe with trades columns + :param dataframe: Dataframe to populate + """ + # Initialize columns with appropriate dtypes + dataframe["trades"] = np.nan + dataframe["orderflow"] = np.nan + dataframe["imbalances"] = np.nan + dataframe["stacked_imbalances_bid"] = np.nan + dataframe["stacked_imbalances_ask"] = np.nan + dataframe["max_delta"] = np.nan + dataframe["min_delta"] = np.nan + dataframe["bid"] = np.nan + dataframe["ask"] = np.nan + dataframe["delta"] = np.nan + dataframe["total_trades"] = np.nan + + # Ensure the 'trades' column is of object type + dataframe["trades"] = dataframe["trades"].astype(object) + dataframe["orderflow"] = dataframe["orderflow"].astype(object) + dataframe["imbalances"] = dataframe["imbalances"].astype(object) + dataframe["stacked_imbalances_bid"] = dataframe["stacked_imbalances_bid"].astype(object) + dataframe["stacked_imbalances_ask"] = dataframe["stacked_imbalances_ask"].astype(object) + + +def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str): + from freqtrade.exchange import timeframe_to_next_date, timeframe_to_resample_freq + + timeframe_frequency = timeframe_to_resample_freq(timeframe) + # calculate ohlcv candle start and end + if df is not None and not df.empty: + df["datetime"] = pd.to_datetime(df["date"], unit="ms") + df["candle_start"] = df["datetime"].dt.floor(timeframe_frequency) + # used in _now_is_time_to_refresh_trades + df["candle_end"] = df["candle_start"].apply( + lambda candle_start: timeframe_to_next_date(timeframe, candle_start) + ) + df.drop(columns=["datetime"], inplace=True) + + +def populate_dataframe_with_trades( + cached_grouped_trades: OrderedDict[Tuple[datetime, datetime], pd.DataFrame], + config, + dataframe: pd.DataFrame, + trades: pd.DataFrame, +) -> Tuple[pd.DataFrame, OrderedDict[Tuple[datetime, datetime], pd.DataFrame]]: + """ + Populates a dataframe with trades + :param dataframe: Dataframe to populate + :param trades: Trades to populate with + :return: Dataframe with trades populated + """ + timeframe = config["timeframe"] + config_orderflow = config["orderflow"] + + # create columns for trades + _init_dataframe_with_trades_columns(dataframe) + + try: + start_time = time.time() + # calculate ohlcv candle start and end + _calculate_ohlcv_candle_start_and_end(trades, timeframe) + + # get date of earliest max_candles candle + max_candles = config_orderflow["max_candles"] + start_date = dataframe.tail(max_candles).date.iat[0] + # slice of trades that are before current ohlcv candles to make groupby faster + trades = trades.loc[trades.candle_start >= start_date] + trades.reset_index(inplace=True, drop=True) + + # group trades by candle start + trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False) + # Create Series to hold complex data + trades_series = pd.Series(index=dataframe.index, dtype=object) + orderflow_series = pd.Series(index=dataframe.index, dtype=object) + imbalances_series = pd.Series(index=dataframe.index, dtype=object) + stacked_imbalances_bid_series = pd.Series(index=dataframe.index, dtype=object) + stacked_imbalances_ask_series = pd.Series(index=dataframe.index, dtype=object) + + trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False) + for candle_start, trades_grouped_df in trades_grouped_by_candle_start: + is_between = candle_start == dataframe["date"] + if is_between.any(): + from freqtrade.exchange import timeframe_to_next_date + + candle_next = timeframe_to_next_date(timeframe, typing.cast(datetime, candle_start)) + if candle_next not in trades_grouped_by_candle_start.groups: + logger.warning( + f"candle at {candle_start} with {len(trades_grouped_df)} trades " + f"might be unfinished, because no finished trades at {candle_next}" + ) + + indices = dataframe.index[is_between].tolist() + # Add trades to each candle + trades_series.loc[indices] = [ + trades_grouped_df.drop(columns=["candle_start", "candle_end"]).to_dict( + orient="records" + ) + ] + # Use caching mechanism + if (candle_start, candle_next) in cached_grouped_trades: + cache_entry = cached_grouped_trades[ + (typing.cast(datetime, candle_start), candle_next) + ] + # dataframe.loc[is_between] = cache_entry # doesn't take, so we need workaround: + # Create a dictionary of the column values to be assigned + update_dict = {c: cache_entry[c].iat[0] for c in cache_entry.columns} + # Assign the values using the update_dict + dataframe.loc[is_between, update_dict.keys()] = pd.DataFrame( + [update_dict], index=dataframe.loc[is_between].index + ) + continue + + # Calculate orderflow for each candle + orderflow = trades_to_volumeprofile_with_total_delta_bid_ask( + trades_grouped_df, scale=config_orderflow["scale"] + ) + orderflow_series.loc[indices] = [orderflow.to_dict(orient="index")] + # Calculate imbalances for each candle's orderflow + imbalances = trades_orderflow_to_imbalances( + orderflow, + imbalance_ratio=config_orderflow["imbalance_ratio"], + imbalance_volume=config_orderflow["imbalance_volume"], + ) + imbalances_series.loc[indices] = [imbalances.to_dict(orient="index")] + + stacked_imbalance_range = config_orderflow["stacked_imbalance_range"] + stacked_imbalances_bid_series.loc[indices] = [ + stacked_imbalance_bid( + imbalances, stacked_imbalance_range=stacked_imbalance_range + ) + ] + stacked_imbalances_ask_series.loc[indices] = [ + stacked_imbalance_ask( + imbalances, stacked_imbalance_range=stacked_imbalance_range + ) + ] + + bid = np.where( + trades_grouped_df["side"].str.contains("sell"), trades_grouped_df["amount"], 0 + ) + + ask = np.where( + trades_grouped_df["side"].str.contains("buy"), trades_grouped_df["amount"], 0 + ) + deltas_per_trade = ask - bid + min_delta = deltas_per_trade.cumsum().min() + max_delta = deltas_per_trade.cumsum().max() + dataframe.loc[indices, "max_delta"] = max_delta + dataframe.loc[indices, "min_delta"] = min_delta + + dataframe.loc[indices, "bid"] = bid.sum() + dataframe.loc[indices, "ask"] = ask.sum() + dataframe.loc[indices, "delta"] = ( + dataframe.loc[indices, "ask"] - dataframe.loc[indices, "bid"] + ) + dataframe.loc[indices, "total_trades"] = len(trades_grouped_df) + + # Cache the result + cached_grouped_trades[(typing.cast(datetime, candle_start), candle_next)] = ( + dataframe.loc[is_between].copy() + ) + + # Maintain cache size + if ( + config.get("runmode") in (RunMode.DRY_RUN, RunMode.LIVE) + and len(cached_grouped_trades) > config_orderflow["cache_size"] + ): + cached_grouped_trades.popitem(last=False) + else: + logger.debug(f"Found NO candles for trades starting with {candle_start}") + logger.debug(f"trades.groups_keys in {time.time() - start_time} seconds") + + # Merge the complex data Series back into the DataFrame + dataframe["trades"] = trades_series + dataframe["orderflow"] = orderflow_series + dataframe["imbalances"] = imbalances_series + dataframe["stacked_imbalances_bid"] = stacked_imbalances_bid_series + dataframe["stacked_imbalances_ask"] = stacked_imbalances_ask_series + + except Exception as e: + logger.exception("Error populating dataframe with trades") + raise DependencyException(e) + + return dataframe, cached_grouped_trades + + +def trades_to_volumeprofile_with_total_delta_bid_ask( + trades: pd.DataFrame, scale: float +) -> pd.DataFrame: + """ + :param trades: dataframe + :param scale: scale aka bin size e.g. 0.5 + :return: trades binned to levels according to scale aka orderflow + """ + df = pd.DataFrame([], columns=DEFAULT_ORDERFLOW_COLUMNS) + # create bid, ask where side is sell or buy + df["bid_amount"] = np.where(trades["side"].str.contains("sell"), trades["amount"], 0) + df["ask_amount"] = np.where(trades["side"].str.contains("buy"), trades["amount"], 0) + df["bid"] = np.where(trades["side"].str.contains("sell"), 1, 0) + df["ask"] = np.where(trades["side"].str.contains("buy"), 1, 0) + # round the prices to the nearest multiple of the scale + df["price"] = ((trades["price"] / scale).round() * scale).astype("float64").values + if df.empty: + df["total"] = np.nan + df["delta"] = np.nan + return df + + df["delta"] = df["ask_amount"] - df["bid_amount"] + df["total_volume"] = df["ask_amount"] + df["bid_amount"] + df["total_trades"] = df["ask"] + df["bid"] + + # group to bins aka apply scale + df = df.groupby("price").sum(numeric_only=True) + return df + + +def trades_orderflow_to_imbalances(df: pd.DataFrame, imbalance_ratio: int, imbalance_volume: int): + """ + :param df: dataframes with bid and ask + :param imbalance_ratio: imbalance_ratio e.g. 3 + :param imbalance_volume: imbalance volume e.g. 10 + :return: dataframe with bid and ask imbalance + """ + bid = df.bid + # compares bid and ask diagonally + ask = df.ask.shift(-1) + bid_imbalance = (bid / ask) > (imbalance_ratio) + # overwrite bid_imbalance with False if volume is not big enough + bid_imbalance_filtered = np.where(df.total_volume < imbalance_volume, False, bid_imbalance) + ask_imbalance = (ask / bid) > (imbalance_ratio) + # overwrite ask_imbalance with False if volume is not big enough + ask_imbalance_filtered = np.where(df.total_volume < imbalance_volume, False, ask_imbalance) + dataframe = pd.DataFrame( + {"bid_imbalance": bid_imbalance_filtered, "ask_imbalance": ask_imbalance_filtered}, + index=df.index, + ) + + return dataframe + + +def stacked_imbalance( + df: pd.DataFrame, label: str, stacked_imbalance_range: int, should_reverse: bool +): + """ + y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1) + https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array + """ + imbalance = df[f"{label}_imbalance"] + int_series = pd.Series(np.where(imbalance, 1, 0)) + stacked = int_series * ( + int_series.groupby((int_series != int_series.shift()).cumsum()).cumcount() + 1 + ) + + max_stacked_imbalance_idx = stacked.index[stacked >= stacked_imbalance_range] + stacked_imbalance_price = np.nan + if not max_stacked_imbalance_idx.empty: + idx = ( + max_stacked_imbalance_idx[0] + if not should_reverse + else np.flipud(max_stacked_imbalance_idx)[0] + ) + stacked_imbalance_price = imbalance.index[idx] + return stacked_imbalance_price + + +def stacked_imbalance_ask(df: pd.DataFrame, stacked_imbalance_range: int): + return stacked_imbalance(df, "ask", stacked_imbalance_range, should_reverse=True) + + +def stacked_imbalance_bid(df: pd.DataFrame, stacked_imbalance_range: int): + return stacked_imbalance(df, "bid", stacked_imbalance_range, should_reverse=False) diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py index 777f99895..809dc5c02 100644 --- a/freqtrade/data/dataprovider.py +++ b/freqtrade/data/dataprovider.py @@ -19,8 +19,8 @@ from freqtrade.constants import ( ListPairsWithTimeframes, PairWithTimeframe, ) -from freqtrade.data.history import load_pair_history -from freqtrade.enums import CandleType, RPCMessageType, RunMode +from freqtrade.data.history import get_datahandler, load_pair_history +from freqtrade.enums import CandleType, RPCMessageType, RunMode, TradingMode from freqtrade.exceptions import ExchangeError, OperationalException from freqtrade.exchange import Exchange, timeframe_to_prev_date, timeframe_to_seconds from freqtrade.exchange.types import OrderBook @@ -445,7 +445,20 @@ class DataProvider: if self._exchange is None: raise OperationalException(NO_EXCHANGE_EXCEPTION) final_pairs = (pairlist + helping_pairs) if helping_pairs else pairlist + # refresh latest ohlcv data self._exchange.refresh_latest_ohlcv(final_pairs) + # refresh latest trades data + self.refresh_latest_trades(pairlist) + + def refresh_latest_trades(self, pairlist: ListPairsWithTimeframes) -> None: + """ + Refresh latest trades data (if enabled in config) + """ + + use_public_trades = self._config.get("exchange", {}).get("use_public_trades", False) + if use_public_trades: + if self._exchange: + self._exchange.refresh_latest_trades(pairlist) @property def available_pairs(self) -> ListPairsWithTimeframes: @@ -483,6 +496,45 @@ class DataProvider: else: return DataFrame() + def trades( + self, pair: str, timeframe: Optional[str] = None, copy: bool = True, candle_type: str = "" + ) -> DataFrame: + """ + Get candle (TRADES) data for the given pair as DataFrame + Please use the `available_pairs` method to verify which pairs are currently cached. + This is not meant to be used in callbacks because of lookahead bias. + :param pair: pair to get the data for + :param timeframe: Timeframe to get data for + :param candle_type: '', mark, index, premiumIndex, or funding_rate + :param copy: copy dataframe before returning if True. + Use False only for read-only operations (where the dataframe is not modified) + """ + if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE): + if self._exchange is None: + raise OperationalException(NO_EXCHANGE_EXCEPTION) + _candle_type = ( + CandleType.from_string(candle_type) + if candle_type != "" + else self._config["candle_type_def"] + ) + return self._exchange.trades( + (pair, timeframe or self._config["timeframe"], _candle_type), copy=copy + ) + elif self.runmode in (RunMode.BACKTEST, RunMode.HYPEROPT): + _candle_type = ( + CandleType.from_string(candle_type) + if candle_type != "" + else self._config["candle_type_def"] + ) + data_handler = get_datahandler( + self._config["datadir"], data_format=self._config["dataformat_trades"] + ) + trades_df = data_handler.trades_load(pair, TradingMode.FUTURES) + return trades_df + + else: + return DataFrame() + def market(self, pair: str) -> Optional[Dict[str, Any]]: """ Return market data for the pair diff --git a/freqtrade/data/entryexitanalysis.py b/freqtrade/data/entryexitanalysis.py index 9d936d295..e76f2dff7 100644 --- a/freqtrade/data/entryexitanalysis.py +++ b/freqtrade/data/entryexitanalysis.py @@ -4,7 +4,6 @@ from typing import List import joblib import pandas as pd -from tabulate import tabulate from freqtrade.configuration import TimeRange from freqtrade.constants import Config @@ -14,6 +13,7 @@ from freqtrade.data.btanalysis import ( load_backtest_stats, ) from freqtrade.exceptions import OperationalException +from freqtrade.util import print_df_rich_table logger = logging.getLogger(__name__) @@ -307,7 +307,7 @@ def _print_table( if name is not None: print(name) - print(tabulate(data, headers="keys", tablefmt="psql", showindex=show_index)) + print_df_rich_table(data, data.keys(), show_index=show_index) def process_entry_exit_reasons(config: Config): diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py index d13fa6fca..bbc9ec4d7 100644 --- a/freqtrade/data/history/history_utils.py +++ b/freqtrade/data/history/history_utils.py @@ -26,8 +26,7 @@ from freqtrade.enums import CandleType, TradingMode from freqtrade.exceptions import OperationalException from freqtrade.exchange import Exchange from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist -from freqtrade.util import dt_ts, format_ms_time -from freqtrade.util.datetime_helpers import dt_now +from freqtrade.util import dt_now, dt_ts, format_ms_time, get_progress_tracker from freqtrade.util.migrations import migrate_data @@ -155,11 +154,9 @@ def refresh_data( :param candle_type: Any of the enum CandleType (must match trading mode!) """ data_handler = get_datahandler(datadir, data_format) - for idx, pair in enumerate(pairs): - process = f"{idx}/{len(pairs)}" + for pair in pairs: _download_pair_history( pair=pair, - process=process, timeframe=timeframe, datadir=datadir, timerange=timerange, @@ -223,7 +220,6 @@ def _download_pair_history( datadir: Path, exchange: Exchange, timeframe: str = "5m", - process: str = "", new_pairs_days: int = 30, data_handler: Optional[IDataHandler] = None, timerange: Optional[TimeRange] = None, @@ -261,7 +257,7 @@ def _download_pair_history( ) logger.info( - f'({process}) - Download history data for "{pair}", {timeframe}, ' + f'Download history data for "{pair}", {timeframe}, ' f"{candle_type} and store in {datadir}. " f'From {format_ms_time(since_ms) if since_ms else "start"} to ' f'{format_ms_time(until_ms) if until_ms else "now"}' @@ -345,53 +341,65 @@ def refresh_backtest_ohlcv_data( pairs_not_available = [] data_handler = get_datahandler(datadir, data_format) candle_type = CandleType.get_default(trading_mode) - process = "" - for idx, pair in enumerate(pairs, start=1): - if pair not in exchange.markets: - pairs_not_available.append(pair) - logger.info(f"Skipping pair {pair}...") - continue - for timeframe in timeframes: - logger.debug(f"Downloading pair {pair}, {candle_type}, interval {timeframe}.") - process = f"{idx}/{len(pairs)}" - _download_pair_history( - pair=pair, - process=process, - datadir=datadir, - exchange=exchange, - timerange=timerange, - data_handler=data_handler, - timeframe=str(timeframe), - new_pairs_days=new_pairs_days, - candle_type=candle_type, - erase=erase, - prepend=prepend, - ) - if trading_mode == "futures": - # Predefined candletype (and timeframe) depending on exchange - # Downloads what is necessary to backtest based on futures data. - tf_mark = exchange.get_option("mark_ohlcv_timeframe") - tf_funding_rate = exchange.get_option("funding_fee_timeframe") + with get_progress_tracker() as progress: + tf_length = len(timeframes) if trading_mode != "futures" else len(timeframes) + 2 + timeframe_task = progress.add_task("Timeframe", total=tf_length) + pair_task = progress.add_task("Downloading data...", total=len(pairs)) - fr_candle_type = CandleType.from_string(exchange.get_option("mark_ohlcv_price")) - # All exchanges need FundingRate for futures trading. - # The timeframe is aligned to the mark-price timeframe. - combs = ((CandleType.FUNDING_RATE, tf_funding_rate), (fr_candle_type, tf_mark)) - for candle_type_f, tf in combs: - logger.debug(f"Downloading pair {pair}, {candle_type_f}, interval {tf}.") + for pair in pairs: + progress.update(pair_task, description=f"Downloading {pair}") + progress.update(timeframe_task, completed=0) + + if pair not in exchange.markets: + pairs_not_available.append(pair) + logger.info(f"Skipping pair {pair}...") + continue + for timeframe in timeframes: + progress.update(timeframe_task, description=f"Timeframe {timeframe}") + logger.debug(f"Downloading pair {pair}, {candle_type}, interval {timeframe}.") _download_pair_history( pair=pair, - process=process, datadir=datadir, exchange=exchange, timerange=timerange, data_handler=data_handler, - timeframe=str(tf), + timeframe=str(timeframe), new_pairs_days=new_pairs_days, - candle_type=candle_type_f, + candle_type=candle_type, erase=erase, prepend=prepend, ) + progress.update(timeframe_task, advance=1) + if trading_mode == "futures": + # Predefined candletype (and timeframe) depending on exchange + # Downloads what is necessary to backtest based on futures data. + tf_mark = exchange.get_option("mark_ohlcv_timeframe") + tf_funding_rate = exchange.get_option("funding_fee_timeframe") + + fr_candle_type = CandleType.from_string(exchange.get_option("mark_ohlcv_price")) + # All exchanges need FundingRate for futures trading. + # The timeframe is aligned to the mark-price timeframe. + combs = ((CandleType.FUNDING_RATE, tf_funding_rate), (fr_candle_type, tf_mark)) + for candle_type_f, tf in combs: + logger.debug(f"Downloading pair {pair}, {candle_type_f}, interval {tf}.") + _download_pair_history( + pair=pair, + datadir=datadir, + exchange=exchange, + timerange=timerange, + data_handler=data_handler, + timeframe=str(tf), + new_pairs_days=new_pairs_days, + candle_type=candle_type_f, + erase=erase, + prepend=prepend, + ) + progress.update( + timeframe_task, advance=1, description=f"Timeframe {candle_type_f}, {tf}" + ) + + progress.update(pair_task, advance=1) + progress.update(timeframe_task, description="Timeframe") return pairs_not_available @@ -480,7 +488,7 @@ def _download_trades_history( return True except Exception: - logger.exception(f'Failed to download historic trades for pair: "{pair}". ') + logger.exception(f'Failed to download and store historic trades for pair: "{pair}". ') return False @@ -501,25 +509,30 @@ def refresh_backtest_trades_data( """ pairs_not_available = [] data_handler = get_datahandler(datadir, data_format=data_format) - for pair in pairs: - if pair not in exchange.markets: - pairs_not_available.append(pair) - logger.info(f"Skipping pair {pair}...") - continue + with get_progress_tracker() as progress: + pair_task = progress.add_task("Downloading data...", total=len(pairs)) + for pair in pairs: + progress.update(pair_task, description=f"Downloading trades [{pair}]") + if pair not in exchange.markets: + pairs_not_available.append(pair) + logger.info(f"Skipping pair {pair}...") + continue - if erase: - if data_handler.trades_purge(pair, trading_mode): - logger.info(f"Deleting existing data for pair {pair}.") + if erase: + if data_handler.trades_purge(pair, trading_mode): + logger.info(f"Deleting existing data for pair {pair}.") + + logger.info(f"Downloading trades for pair {pair}.") + _download_trades_history( + exchange=exchange, + pair=pair, + new_pairs_days=new_pairs_days, + timerange=timerange, + data_handler=data_handler, + trading_mode=trading_mode, + ) + progress.update(pair_task, advance=1) - logger.info(f"Downloading trades for pair {pair}.") - _download_trades_history( - exchange=exchange, - pair=pair, - new_pairs_days=new_pairs_days, - timerange=timerange, - data_handler=data_handler, - trading_mode=trading_mode, - ) return pairs_not_available diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py index 6510e8d4f..fc92ed5a9 100644 --- a/freqtrade/exchange/__init__.py +++ b/freqtrade/exchange/__init__.py @@ -11,6 +11,7 @@ from freqtrade.exchange.bitpanda import Bitpanda from freqtrade.exchange.bitvavo import Bitvavo from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.coinbasepro import Coinbasepro +from freqtrade.exchange.cryptocom import Cryptocom from freqtrade.exchange.exchange_utils import ( ROUND_DOWN, ROUND_UP, diff --git a/freqtrade/exchange/binance_leverage_tiers.json b/freqtrade/exchange/binance_leverage_tiers.json index ae797c9e5..91775fede 100644 --- a/freqtrade/exchange/binance_leverage_tiers.json +++ b/freqtrade/exchange/binance_leverage_tiers.json @@ -1777,14 +1777,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -1792,80 +1792,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 15.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "15", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "333250.0" } } ], @@ -2639,14 +2671,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -2654,80 +2686,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "3000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "333250.0" } } ], @@ -2752,80 +2816,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "50.0" + "maintMarginRatio": "0.02", + "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "3", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", "minNotional": 25000.0, "maxNotional": 200000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { - "bracket": "3", + "bracket": "4", "initialLeverage": "10", "notionalCap": "200000", "notionalFloor": "25000", "maintMarginRatio": "0.05", - "cum": "675.0" - } - }, - { - "tier": 4.0, - "currency": "USDT", - "minNotional": 200000.0, - "maxNotional": 500000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "500000", - "notionalFloor": "200000", - "maintMarginRatio": "0.1", - "cum": "10675.0" + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 500000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "500000", - "maintMarginRatio": "0.125", - "cum": "23175.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "398175.0" + "cum": "333250.0" } } ], @@ -5760,80 +5856,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "50.0" + "maintMarginRatio": "0.02", + "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "675.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5675.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11925.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386925.0" + "cum": "333250.0" } } ], @@ -5858,80 +5986,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "50.0" + "maintMarginRatio": "0.02", + "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "675.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5675.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11925.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386925.0" + "cum": "333250.0" } } ], @@ -7860,10 +8020,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.015, - "maxLeverage": 50.0, + "maxLeverage": 21.0, "info": { "bracket": "1", - "initialLeverage": "50", + "initialLeverage": "21", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.015", @@ -10023,14 +10183,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 25.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "25", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -10038,38 +10198,168 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 200000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "200000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "4", + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "5", + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "8", + "initialLeverage": "1", + "notionalCap": "2000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.5", + "cum": "333250.0" + } + } + ], + "CRV/USDC:USDC": [ + { + "tier": 1.0, + "currency": "USDC", + "minNotional": 0.0, + "maxNotional": 50000.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "50000", + "notionalFloor": "0", + "maintMarginRatio": "0.01", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDC", + "minNotional": 50000.0, + "maxNotional": 150000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "150000", + "notionalFloor": "50000", + "maintMarginRatio": "0.025", + "cum": "750.0" + } + }, + { + "tier": 3.0, + "currency": "USDC", + "minNotional": 150000.0, + "maxNotional": 250000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "250000", + "notionalFloor": "150000", + "maintMarginRatio": "0.05", + "cum": "4500.0" + } + }, + { + "tier": 4.0, + "currency": "USDC", + "minNotional": 250000.0, "maxNotional": 500000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, @@ -10077,41 +10367,57 @@ "bracket": "4", "initialLeverage": "5", "notionalCap": "500000", - "notionalFloor": "200000", + "notionalFloor": "250000", "maintMarginRatio": "0.1", - "cum": "10650.0" + "cum": "17000.0" } }, { "tier": 5.0, - "currency": "USDT", + "currency": "USDC", "minNotional": 500000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", "notionalFloor": "500000", "maintMarginRatio": "0.125", - "cum": "23150.0" + "cum": "29500.0" } }, { "tier": 6.0, - "currency": "USDT", + "currency": "USDC", "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "154500.0" + } + }, + { + "tier": 7.0, + "currency": "USDC", + "minNotional": 3000000.0, "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "398150.0" + "cum": "904500.0" } } ], @@ -14882,80 +15188,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "50.0" + "maintMarginRatio": "0.02", + "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "675.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5675.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11925.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "3000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386925.0" + "cum": "333250.0" } } ], @@ -15488,10 +15826,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 11.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "11", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -16399,14 +16737,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -16414,80 +16752,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "3000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "333250.0" } } ], @@ -17263,14 +17633,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -17278,80 +17648,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - 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"LIT/USDT:USDT": [ - { - "tier": 1.0, - "currency": "USDT", - "minNotional": 0.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, - "info": { - "bracket": "1", - "initialLeverage": "20", - "notionalCap": "25000", - "notionalFloor": "0", - "maintMarginRatio": "0.025", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, - "info": { - "bracket": "2", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "625.0" - } - }, - { - "tier": 3.0, - "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "3", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5625.0" - } - }, - { - "tier": 4.0, - "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "4", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11875.0" - } - }, - { - "tier": 5.0, - "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "5", - "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386875.0" - } - } - ], "LOOM/USDT:USDT": [ { "tier": 1.0, @@ -28017,14 +28531,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -28032,80 +28546,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "3000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "333250.0" } } ], @@ -28588,10 +29134,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.015, - "maxLeverage": 50.0, + "maxLeverage": 26.0, "info": { "bracket": "1", - "initialLeverage": "50", + "initialLeverage": "26", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.015", @@ -28698,13 +29244,13 @@ "tier": 8.0, "currency": "USDT", "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "8", "initialLeverage": "1", - "notionalCap": "10000000", + "notionalCap": "6000000", "notionalFloor": "5000000", "maintMarginRatio": "0.5", "cum": "1665275.0" @@ -29188,80 +29734,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "50.0" + "maintMarginRatio": "0.02", + "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "675.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5675.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11925.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386925.0" + "cum": "333250.0" } } ], @@ -30653,14 +31231,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -30668,80 +31246,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "3000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "333250.0" } } ], @@ -32363,14 +32973,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.015", "cum": "0.0" } }, @@ -32378,80 +32988,112 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 15.0, + "maxNotional": 20000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "15", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "20000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", + "maintMarginRatio": "0.02", "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "minNotional": 20000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "20000", + "maintMarginRatio": "0.025", + "cum": "125.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 200000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" + "initialLeverage": "5", + "notionalCap": "400000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10750.0" } }, { "tier": 6.0, "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "500000", + "notionalFloor": "400000", + "maintMarginRatio": "0.125", + "cum": "20750.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.25", + "cum": "83250.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "2000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "333250.0" } } ], @@ -37842,13 +38484,13 @@ "tier": 2.0, "currency": "USDT", "minNotional": 5000.0, - "maxNotional": 20000.0, + "maxNotional": 25000.0, "maintenanceMarginRate": 0.02, "maxLeverage": 25.0, "info": { "bracket": "2", "initialLeverage": "25", - "notionalCap": "20000", + "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.02", "cum": "25.0" @@ -37857,101 +38499,231 @@ { "tier": 3.0, "currency": "USDT", - "minNotional": 20000.0, - "maxNotional": 25000.0, + "minNotional": 25000.0, + "maxNotional": 50000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 20.0, "info": { "bracket": "3", "initialLeverage": "20", - "notionalCap": "25000", - "notionalFloor": "20000", + "notionalCap": "50000", + "notionalFloor": "25000", "maintMarginRatio": "0.025", - "cum": "125.0" + "cum": "150.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 200000.0, + "minNotional": 50000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "200000", - "notionalFloor": "25000", + "notionalCap": "500000", + "notionalFloor": "50000", "maintMarginRatio": "0.05", - "cum": "750.0" + "cum": "1400.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 200000.0, - "maxNotional": 400000.0, + "minNotional": 500000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "5", "initialLeverage": "5", - "notionalCap": "400000", - "notionalFloor": "200000", + "notionalCap": "1000000", + "notionalFloor": "500000", "maintMarginRatio": "0.1", - "cum": "10750.0" + "cum": "26400.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 400000.0, - "maxNotional": 500000.0, + "minNotional": 1000000.0, + "maxNotional": 1250000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "6", "initialLeverage": "4", - "notionalCap": "500000", - "notionalFloor": "400000", + "notionalCap": "1250000", + "notionalFloor": "1000000", "maintMarginRatio": "0.125", - "cum": "20750.0" + "cum": "51400.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 500000.0, - "maxNotional": 1000000.0, + "minNotional": 1250000.0, + "maxNotional": 2500000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "7", "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "500000", + "notionalCap": "2500000", + "notionalFloor": "1250000", "maintMarginRatio": "0.25", - "cum": "83250.0" + "cum": "207650.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "minNotional": 2500000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "8", "initialLeverage": "1", - "notionalCap": "2000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "2500000", "maintMarginRatio": "0.5", - "cum": "333250.0" + "cum": "832650.0" } } ], "ZRO/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.015, + "maxLeverage": 50.0, + "info": { + "bracket": "1", + "initialLeverage": "50", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.015", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 50000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, + "info": { + "bracket": "2", + "initialLeverage": "25", + "notionalCap": "50000", + "notionalFloor": "5000", + "maintMarginRatio": "0.02", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 50000.0, + "maxNotional": 100000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "3", + "initialLeverage": "20", + "notionalCap": "100000", + "notionalFloor": "50000", + "maintMarginRatio": "0.025", + "cum": "275.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 100000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "4", + "initialLeverage": "10", + "notionalCap": "1000000", + "notionalFloor": "100000", + "maintMarginRatio": "0.05", + "cum": "2775.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "5", + "initialLeverage": "5", + "notionalCap": "2000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.1", + "cum": "52775.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 2000000.0, + "maxNotional": 2500000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "2500000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.125", + "cum": "102775.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 2500000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "5000000", + "notionalFloor": "2500000", + "maintMarginRatio": "0.25", + "cum": "415275.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", + "minNotional": 5000000.0, + "maxNotional": 10000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "8", + "initialLeverage": "1", + "notionalCap": "10000000", + "notionalFloor": "5000000", + "maintMarginRatio": "0.5", + "cum": "1665275.0" + } + } + ], + "ZRX/USDT:USDT": [ { "tier": 1.0, "currency": "USDT", @@ -38080,103 +38852,5 @@ "cum": "333250.0" } } - ], - "ZRX/USDT:USDT": [ - { - "tier": 1.0, - "currency": "USDT", - "minNotional": 0.0, - "maxNotional": 5000.0, - "maintenanceMarginRate": 0.015, - "maxLeverage": 50.0, - "info": { - "bracket": "1", - "initialLeverage": "50", - "notionalCap": "5000", - "notionalFloor": "0", - "maintMarginRatio": "0.015", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "USDT", - "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, - "info": { - "bracket": "2", - "initialLeverage": "20", - "notionalCap": "25000", - "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "50.0" - } - }, - { - "tier": 3.0, - "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, - "info": { - "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "675.0" - } - }, - { - "tier": 4.0, - "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5675.0" - } - }, - { - "tier": 5.0, - "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11925.0" - } - }, - { - "tier": 6.0, - "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "6", - "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386925.0" - } - } ] } \ No newline at end of file diff --git a/freqtrade/exchange/common.py b/freqtrade/exchange/common.py index 21c67d291..224f24efc 100644 --- a/freqtrade/exchange/common.py +++ b/freqtrade/exchange/common.py @@ -37,7 +37,6 @@ API_FETCH_ORDER_RETRY_COUNT = 5 BAD_EXCHANGES = { "bitmex": "Various reasons.", - "phemex": "Does not provide history.", "probit": "Requires additional, regular calls to `signIn()`.", "poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.", } diff --git a/freqtrade/exchange/cryptocom.py b/freqtrade/exchange/cryptocom.py new file mode 100644 index 000000000..56b007b07 --- /dev/null +++ b/freqtrade/exchange/cryptocom.py @@ -0,0 +1,19 @@ +"""Crypto.com exchange subclass""" + +import logging +from typing import Dict + +from freqtrade.exchange import Exchange + + +logger = logging.getLogger(__name__) + + +class Cryptocom(Exchange): + """Crypto.com exchange class. + Contains adjustments needed for Freqtrade to work with this exchange. + """ + + _ft_has: Dict = { + "ohlcv_candle_limit": 300, + } diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 34ca8a3e8..3ed35b03d 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -22,6 +22,7 @@ from pandas import DataFrame, concat from freqtrade.constants import ( DEFAULT_AMOUNT_RESERVE_PERCENT, + DEFAULT_TRADES_COLUMNS, NON_OPEN_EXCHANGE_STATES, BidAsk, BuySell, @@ -33,7 +34,13 @@ from freqtrade.constants import ( OBLiteral, PairWithTimeframe, ) -from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list +from freqtrade.data.converter import ( + clean_ohlcv_dataframe, + ohlcv_to_dataframe, + trades_df_remove_duplicates, + trades_dict_to_list, + trades_list_to_df, +) from freqtrade.enums import ( OPTIMIZE_MODES, TRADE_MODES, @@ -124,6 +131,7 @@ class Exchange: "tickers_have_percentage": True, "tickers_have_bid_ask": True, # bid / ask empty for fetch_tickers "tickers_have_price": True, + "trades_limit": 1000, # Limit for 1 call to fetch_trades "trades_pagination": "time", # Possible are "time" or "id" "trades_pagination_arg": "since", "trades_has_history": False, @@ -195,6 +203,9 @@ class Exchange: self._klines: Dict[PairWithTimeframe, DataFrame] = {} self._expiring_candle_cache: Dict[Tuple[str, int], PeriodicCache] = {} + # Holds public_trades + self._trades: Dict[PairWithTimeframe, DataFrame] = {} + # Holds all open sell orders for dry_run self._dry_run_open_orders: Dict[str, Any] = {} @@ -223,6 +234,8 @@ class Exchange: # Assign this directly for easy access self._ohlcv_partial_candle = self._ft_has["ohlcv_partial_candle"] + self._max_trades_limit = self._ft_has["trades_limit"] + self._trades_pagination = self._ft_has["trades_pagination"] self._trades_pagination_arg = self._ft_has["trades_pagination_arg"] @@ -316,6 +329,7 @@ class Exchange: self.validate_trading_mode_and_margin_mode(self.trading_mode, self.margin_mode) self.validate_pricing(config["exit_pricing"]) self.validate_pricing(config["entry_pricing"]) + self.validate_orderflow(config["exchange"]) def _init_ccxt( self, exchange_config: Dict[str, Any], sync: bool, ccxt_kwargs: Dict[str, Any] @@ -339,10 +353,14 @@ class Exchange: raise OperationalException(f"Exchange {name} is not supported by ccxt") ex_config = { - "apiKey": exchange_config.get("key"), + "apiKey": exchange_config.get("apiKey", exchange_config.get("key")), "secret": exchange_config.get("secret"), "password": exchange_config.get("password"), "uid": exchange_config.get("uid", ""), + "accountId": exchange_config.get("accountId", ""), + # DEX attributes: + "walletAddress": exchange_config.get("walletAddress"), + "privateKey": exchange_config.get("privateKey"), } if ccxt_kwargs: logger.info("Applying additional ccxt config: %s", ccxt_kwargs) @@ -517,6 +535,15 @@ class Exchange: else: return DataFrame() + def trades(self, pair_interval: PairWithTimeframe, copy: bool = True) -> DataFrame: + if pair_interval in self._trades: + if copy: + return self._trades[pair_interval].copy() + else: + return self._trades[pair_interval] + else: + return DataFrame() + def get_contract_size(self, pair: str) -> Optional[float]: if self.trading_mode == TradingMode.FUTURES: market = self.markets.get(pair, {}) @@ -770,6 +797,14 @@ class Exchange: f"Time in force policies are not supported for {self.name} yet." ) + def validate_orderflow(self, exchange: Dict) -> None: + if exchange.get("use_public_trades", False) and ( + not self.exchange_has("fetchTrades") or not self._ft_has["trades_has_history"] + ): + raise ConfigurationError( + f"Trade data not available for {self.name}. Can't use orderflow feature." + ) + def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> int: """ Checks if required startup_candles is more than ohlcv_candle_limit(). @@ -2597,6 +2632,171 @@ class Exchange: data = [[x["timestamp"], x["fundingRate"], 0, 0, 0, 0] for x in data] return data + # fetch Trade data stuff + + def needed_candle_for_trades_ms(self, timeframe: str, candle_type: CandleType) -> int: + candle_limit = self.ohlcv_candle_limit(timeframe, candle_type) + tf_s = timeframe_to_seconds(timeframe) + candles_fetched = candle_limit * self.required_candle_call_count + + max_candles = self._config["orderflow"]["max_candles"] + + required_candles = min(max_candles, candles_fetched) + move_to = ( + tf_s * candle_limit * required_candles + if required_candles > candle_limit + else (max_candles + 1) * tf_s + ) + + now = timeframe_to_next_date(timeframe) + return int((now - timedelta(seconds=move_to)).timestamp() * 1000) + + def _process_trades_df( + self, + pair: str, + timeframe: str, + c_type: CandleType, + ticks: List[List], + cache: bool, + first_required_candle_date: int, + ) -> DataFrame: + # keeping parsed dataframe in cache + trades_df = trades_list_to_df(ticks, True) + + if cache: + if (pair, timeframe, c_type) in self._trades: + old = self._trades[(pair, timeframe, c_type)] + # Reassign so we return the updated, combined df + combined_df = concat([old, trades_df], axis=0) + logger.debug(f"Clean duplicated ticks from Trades data {pair}") + trades_df = DataFrame( + trades_df_remove_duplicates(combined_df), columns=combined_df.columns + ) + # Age out old candles + trades_df = trades_df[first_required_candle_date < trades_df["timestamp"]] + trades_df = trades_df.reset_index(drop=True) + self._trades[(pair, timeframe, c_type)] = trades_df + return trades_df + + def refresh_latest_trades( + self, + pair_list: ListPairsWithTimeframes, + *, + cache: bool = True, + ) -> Dict[PairWithTimeframe, DataFrame]: + """ + Refresh in-memory TRADES asynchronously and set `_trades` with the result + Loops asynchronously over pair_list and downloads all pairs async (semi-parallel). + Only used in the dataprovider.refresh() method. + :param pair_list: List of 3 element tuples containing (pair, timeframe, candle_type) + :param cache: Assign result to _trades. Useful for one-off downloads like for pairlists + :return: Dict of [{(pair, timeframe): Dataframe}] + """ + from freqtrade.data.history import get_datahandler + + data_handler = get_datahandler( + self._config["datadir"], data_format=self._config["dataformat_trades"] + ) + logger.debug("Refreshing TRADES data for %d pairs", len(pair_list)) + since_ms = None + results_df = {} + for pair, timeframe, candle_type in set(pair_list): + new_ticks: List = [] + all_stored_ticks_df = DataFrame(columns=DEFAULT_TRADES_COLUMNS + ["date"]) + first_candle_ms = self.needed_candle_for_trades_ms(timeframe, candle_type) + # refresh, if + # a. not in _trades + # b. no cache used + # c. need new data + is_in_cache = (pair, timeframe, candle_type) in self._trades + if ( + not is_in_cache + or not cache + or self._now_is_time_to_refresh_trades(pair, timeframe, candle_type) + ): + logger.debug(f"Refreshing TRADES data for {pair}") + # fetch trades since latest _trades and + # store together with existing trades + try: + until = None + from_id = None + if is_in_cache: + from_id = self._trades[(pair, timeframe, candle_type)].iloc[-1]["id"] + until = dt_ts() # now + + else: + until = int(timeframe_to_prev_date(timeframe).timestamp()) * 1000 + all_stored_ticks_df = data_handler.trades_load( + f"{pair}-cached", self.trading_mode + ) + + if not all_stored_ticks_df.empty: + if ( + all_stored_ticks_df.iloc[-1]["timestamp"] > first_candle_ms + and all_stored_ticks_df.iloc[0]["timestamp"] <= first_candle_ms + ): + # Use cache and populate further + last_cached_ms = all_stored_ticks_df.iloc[-1]["timestamp"] + from_id = all_stored_ticks_df.iloc[-1]["id"] + # only use cached if it's closer than first_candle_ms + since_ms = ( + last_cached_ms + if last_cached_ms > first_candle_ms + else first_candle_ms + ) + else: + # Skip cache, it's too old + all_stored_ticks_df = DataFrame( + columns=DEFAULT_TRADES_COLUMNS + ["date"] + ) + + # from_id overrules with exchange set to id paginate + [_, new_ticks] = self.get_historic_trades( + pair, + since=since_ms if since_ms else first_candle_ms, + until=until, + from_id=from_id, + ) + + except Exception: + logger.exception(f"Refreshing TRADES data for {pair} failed") + continue + + if new_ticks: + all_stored_ticks_list = all_stored_ticks_df[ + DEFAULT_TRADES_COLUMNS + ].values.tolist() + all_stored_ticks_list.extend(new_ticks) + trades_df = self._process_trades_df( + pair, + timeframe, + candle_type, + all_stored_ticks_list, + cache, + first_required_candle_date=first_candle_ms, + ) + results_df[(pair, timeframe, candle_type)] = trades_df + data_handler.trades_store( + f"{pair}-cached", trades_df[DEFAULT_TRADES_COLUMNS], self.trading_mode + ) + + else: + logger.error(f"No new ticks for {pair}") + + return results_df + + def _now_is_time_to_refresh_trades( + self, pair: str, timeframe: str, candle_type: CandleType + ) -> bool: # Timeframe in seconds + trades = self.trades((pair, timeframe, candle_type), False) + pair_last_refreshed = int(trades.iloc[-1]["timestamp"]) + full_candle = ( + int(timeframe_to_next_date(timeframe, dt_from_ts(pair_last_refreshed)).timestamp()) + * 1000 + ) + now = dt_ts() + return full_candle <= now + # Fetch historic trades @retrier_async @@ -2611,10 +2811,11 @@ class Exchange: returns: List of dicts containing trades, the next iteration value (new "since" or trade_id) """ try: + trades_limit = self._max_trades_limit # fetch trades asynchronously if params: logger.debug("Fetching trades for pair %s, params: %s ", pair, params) - trades = await self._api_async.fetch_trades(pair, params=params, limit=1000) + trades = await self._api_async.fetch_trades(pair, params=params, limit=trades_limit) else: logger.debug( "Fetching trades for pair %s, since %s %s...", @@ -2622,7 +2823,7 @@ class Exchange: since, "(" + dt_from_ts(since).isoformat() + ") " if since is not None else "", ) - trades = await self._api_async.fetch_trades(pair, since=since, limit=1000) + trades = await self._api_async.fetch_trades(pair, since=since, limit=trades_limit) trades = self._trades_contracts_to_amount(trades) pagination_value = self._get_trade_pagination_next_value(trades) return trades_dict_to_list(trades), pagination_value @@ -3417,13 +3618,12 @@ class Exchange: def get_maintenance_ratio_and_amt( self, pair: str, - nominal_value: float, + notional_value: float, ) -> Tuple[float, Optional[float]]: """ Important: Must be fetching data from cached values as this is used by backtesting! :param pair: Market symbol - :param nominal_value: The total trade amount in quote currency including leverage - maintenance amount only on Binance + :param notional_value: The total trade amount in quote currency :return: (maintenance margin ratio, maintenance amount) """ @@ -3440,7 +3640,7 @@ class Exchange: pair_tiers = self._leverage_tiers[pair] for tier in reversed(pair_tiers): - if nominal_value >= tier["minNotional"]: + if notional_value >= tier["minNotional"]: return (tier["maintenanceMarginRate"], tier["maintAmt"]) raise ExchangeError("nominal value can not be lower than 0") @@ -3448,4 +3648,3 @@ class Exchange: # describes the min amt for a tier, and the lowest tier will always go down to 0 else: raise ExchangeError(f"Cannot get maintenance ratio using {self.name}") - raise ExchangeError(f"Cannot get maintenance ratio using {self.name}") diff --git a/freqtrade/exchange/exchange_ws.py b/freqtrade/exchange/exchange_ws.py index 06ac45e21..0c093171b 100644 --- a/freqtrade/exchange/exchange_ws.py +++ b/freqtrade/exchange/exchange_ws.py @@ -78,6 +78,12 @@ class ExchangeWS: finally: self.__cleanup_called = True + def _pop_history(self, paircomb: PairWithTimeframe) -> None: + """ + Remove history for a pair/timeframe combination from ccxt cache + """ + self.ccxt_object.ohlcvs.get(paircomb[0], {}).pop(paircomb[1], None) + def cleanup_expired(self) -> None: """ Remove pairs from watchlist if they've not been requested within @@ -89,8 +95,10 @@ class ExchangeWS: timeframe_s = timeframe_to_seconds(timeframe) last_refresh = self.klines_last_request.get(p, 0) if last_refresh > 0 and (dt_ts() - last_refresh) > ((timeframe_s + 20) * 1000): - logger.info(f"Removing {p} from watchlist") + logger.info(f"Removing {p} from websocket watchlist.") self._klines_watching.discard(p) + # Pop history to avoid getting stale data + self._pop_history(p) changed = True if changed: logger.info(f"Removal done: new watch list ({len(self._klines_watching)})") @@ -128,6 +136,7 @@ class ExchangeWS: logger.info(f"{pair}, {timeframe}, {candle_type} - Task finished - {result}") self._klines_scheduled.discard((pair, timeframe, candle_type)) + self._pop_history((pair, timeframe, candle_type)) async def _continuously_async_watch_ohlcv( self, pair: str, timeframe: str, candle_type: CandleType diff --git a/freqtrade/exchange/hyperliquid.py b/freqtrade/exchange/hyperliquid.py new file mode 100644 index 000000000..d2b1e1482 --- /dev/null +++ b/freqtrade/exchange/hyperliquid.py @@ -0,0 +1,24 @@ +"""Hyperliquid exchange subclass""" + +import logging +from typing import Dict + +from freqtrade.exchange import Exchange + + +logger = logging.getLogger(__name__) + + +class Hyperliquid(Exchange): + """Hyperliquid exchange class. + Contains adjustments needed for Freqtrade to work with this exchange. + """ + + _ft_has: Dict = { + # Only the most recent 5000 candles are available according to the + # exchange's API documentation. + "ohlcv_has_history": True, + "ohlcv_candle_limit": 5000, + "trades_has_history": False, # Trades endpoint doesn't seem available. + "exchange_has_overrides": {"fetchTrades": False}, + } diff --git a/freqtrade/optimize/analysis/lookahead_helpers.py b/freqtrade/optimize/analysis/lookahead_helpers.py index c0e6fa1ba..a8fb1cd35 100644 --- a/freqtrade/optimize/analysis/lookahead_helpers.py +++ b/freqtrade/optimize/analysis/lookahead_helpers.py @@ -4,11 +4,13 @@ from pathlib import Path from typing import Any, Dict, List import pandas as pd +from rich.text import Text from freqtrade.constants import Config from freqtrade.exceptions import OperationalException from freqtrade.optimize.analysis.lookahead import LookaheadAnalysis from freqtrade.resolvers import StrategyResolver +from freqtrade.util import print_rich_table logger = logging.getLogger(__name__) @@ -53,18 +55,18 @@ class LookaheadAnalysisSubFunctions: [ inst.strategy_obj["location"].parts[-1], inst.strategy_obj["name"], - inst.current_analysis.has_bias, + Text("Yes", style="bold red") + if inst.current_analysis.has_bias + else Text("No", style="bold green"), inst.current_analysis.total_signals, inst.current_analysis.false_entry_signals, inst.current_analysis.false_exit_signals, ", ".join(inst.current_analysis.false_indicators), ] ) - from tabulate import tabulate - table = tabulate(data, headers=headers, tablefmt="orgtbl") - print(table) - return table, headers, data + print_rich_table(data, headers, summary="Lookahead Analysis") + return data @staticmethod def export_to_csv(config: Dict[str, Any], lookahead_analysis: List[LookaheadAnalysis]): diff --git a/freqtrade/optimize/analysis/recursive_helpers.py b/freqtrade/optimize/analysis/recursive_helpers.py index cde1a214e..be596fa68 100644 --- a/freqtrade/optimize/analysis/recursive_helpers.py +++ b/freqtrade/optimize/analysis/recursive_helpers.py @@ -7,6 +7,7 @@ from freqtrade.constants import Config from freqtrade.exceptions import OperationalException from freqtrade.optimize.analysis.recursive import RecursiveAnalysis from freqtrade.resolvers import StrategyResolver +from freqtrade.util import print_rich_table logger = logging.getLogger(__name__) @@ -16,9 +17,9 @@ class RecursiveAnalysisSubFunctions: @staticmethod def text_table_recursive_analysis_instances(recursive_instances: List[RecursiveAnalysis]): startups = recursive_instances[0]._startup_candle - headers = ["indicators"] + headers = ["Indicators"] for candle in startups: - headers.append(candle) + headers.append(str(candle)) data = [] for inst in recursive_instances: @@ -30,13 +31,11 @@ class RecursiveAnalysisSubFunctions: data.append(temp_data) if len(data) > 0: - from tabulate import tabulate + print_rich_table(data, headers, summary="Recursive Analysis") - table = tabulate(data, headers=headers, tablefmt="orgtbl") - print(table) - return table, headers, data + return data - return None, None, data + return data @staticmethod def calculate_config_overrides(config: Config): diff --git a/freqtrade/optimize/edge_cli.py b/freqtrade/optimize/edge_cli.py index 9bd8ff1c9..65dd30efc 100644 --- a/freqtrade/optimize/edge_cli.py +++ b/freqtrade/optimize/edge_cli.py @@ -52,4 +52,4 @@ class EdgeCli: result = self.edge.calculate(self.config["exchange"]["pair_whitelist"]) if result: print("") # blank line for readability - print(generate_edge_table(self.edge._cached_pairs)) + generate_edge_table(self.edge._cached_pairs) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index b19fca9dc..2006cee58 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -14,19 +14,11 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Tuple import rapidjson -from colorama import init as colorama_init from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects from joblib.externals import cloudpickle from pandas import DataFrame -from rich.progress import ( - BarColumn, - MofNCompleteColumn, - Progress, - TaskProgressColumn, - TextColumn, - TimeElapsedColumn, - TimeRemainingColumn, -) +from rich.align import Align +from rich.console import Console from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config from freqtrade.data.converter import trim_dataframes @@ -40,6 +32,7 @@ from freqtrade.optimize.backtesting import Backtesting # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules from freqtrade.optimize.hyperopt_auto import HyperOptAuto from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss +from freqtrade.optimize.hyperopt_output import HyperoptOutput from freqtrade.optimize.hyperopt_tools import ( HyperoptStateContainer, HyperoptTools, @@ -47,6 +40,7 @@ from freqtrade.optimize.hyperopt_tools import ( ) from freqtrade.optimize.optimize_reports import generate_strategy_stats from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver +from freqtrade.util import get_progress_tracker # Suppress scikit-learn FutureWarnings from skopt @@ -86,6 +80,8 @@ class Hyperopt: self.max_open_trades_space: List[Dimension] = [] self.dimensions: List[Dimension] = [] + self._hyper_out: HyperoptOutput = HyperoptOutput() + self.config = config self.min_date: datetime self.max_date: datetime @@ -260,7 +256,7 @@ class Hyperopt: result["max_open_trades"] = {"max_open_trades": strategy.max_open_trades} return result - def print_results(self, results) -> None: + def print_results(self, results: Dict[str, Any]) -> None: """ Log results if it is better than any previous evaluation TODO: this should be moved to HyperoptTools too @@ -268,17 +264,12 @@ class Hyperopt: is_best = results["is_best"] if self.print_all or is_best: - print( - HyperoptTools.get_result_table( - self.config, - results, - self.total_epochs, - self.print_all, - self.print_colorized, - self.hyperopt_table_header, - ) + self._hyper_out.add_data( + self.config, + [results], + self.total_epochs, + self.print_all, ) - self.hyperopt_table_header = 2 def init_spaces(self): """ @@ -626,25 +617,18 @@ class Hyperopt: self.opt = self.get_optimizer(self.dimensions, config_jobs) - if self.print_colorized: - colorama_init(autoreset=True) - try: with Parallel(n_jobs=config_jobs) as parallel: jobs = parallel._effective_n_jobs() logger.info(f"Effective number of parallel workers used: {jobs}") + console = Console( + color_system="auto" if self.print_colorized else None, + ) # Define progressbar - with Progress( - TextColumn("[progress.description]{task.description}"), - BarColumn(bar_width=None), - MofNCompleteColumn(), - TaskProgressColumn(), - "•", - TimeElapsedColumn(), - "•", - TimeRemainingColumn(), - expand=True, + with get_progress_tracker( + console=console, + cust_objs=[Align.center(self._hyper_out.table)], ) as pbar: task = pbar.add_task("Epochs", total=self.total_epochs) diff --git a/freqtrade/optimize/hyperopt_output.py b/freqtrade/optimize/hyperopt_output.py new file mode 100644 index 000000000..72e049745 --- /dev/null +++ b/freqtrade/optimize/hyperopt_output.py @@ -0,0 +1,123 @@ +import sys +from typing import List, Optional, Union + +from rich.console import Console +from rich.table import Table +from rich.text import Text + +from freqtrade.constants import Config +from freqtrade.optimize.optimize_reports import generate_wins_draws_losses +from freqtrade.util import fmt_coin + + +class HyperoptOutput: + def __init__(self): + self.table = Table( + title="Hyperopt results", + ) + # Headers + self.table.add_column("Best", justify="left") + self.table.add_column("Epoch", justify="right") + self.table.add_column("Trades", justify="right") + self.table.add_column("Win Draw Loss Win%", justify="right") + self.table.add_column("Avg profit", justify="right") + self.table.add_column("Profit", justify="right") + self.table.add_column("Avg duration", justify="right") + self.table.add_column("Objective", justify="right") + self.table.add_column("Max Drawdown (Acct)", justify="right") + + def _add_row(self, data: List[Union[str, Text]]): + """Add single row""" + row_to_add: List[Union[str, Text]] = [r if isinstance(r, Text) else str(r) for r in data] + + self.table.add_row(*row_to_add) + + def _add_rows(self, data: List[List[Union[str, Text]]]): + """add multiple rows""" + for row in data: + self._add_row(row) + + def print(self, console: Optional[Console] = None, *, print_colorized=True): + if not console: + console = Console( + color_system="auto" if print_colorized else None, + width=200 if "pytest" in sys.modules else None, + ) + + console.print(self.table) + + def add_data( + self, + config: Config, + results: list, + total_epochs: int, + highlight_best: bool, + ) -> None: + """Format one or multiple rows and add them""" + stake_currency = config["stake_currency"] + + for r in results: + self.table.add_row( + *[ + # "Best": + ( + ("*" if r["is_initial_point"] or r["is_random"] else "") + + (" Best" if r["is_best"] else "") + ).lstrip(), + # "Epoch": + f"{r['current_epoch']}/{total_epochs}", + # "Trades": + str(r["results_metrics"]["total_trades"]), + # "Win Draw Loss Win%": + generate_wins_draws_losses( + r["results_metrics"]["wins"], + r["results_metrics"]["draws"], + r["results_metrics"]["losses"], + ), + # "Avg profit": + f"{r['results_metrics']['profit_mean']:.2%}" + if r["results_metrics"]["profit_mean"] is not None + else "--", + # "Profit": + Text( + "{} {}".format( + fmt_coin( + r["results_metrics"]["profit_total_abs"], + stake_currency, + keep_trailing_zeros=True, + ), + f"({r['results_metrics']['profit_total']:,.2%})".rjust(10, " "), + ) + if r["results_metrics"].get("profit_total_abs", 0) != 0.0 + else "--", + style=( + "green" + if r["results_metrics"].get("profit_total_abs", 0) > 0 + else "red" + ) + if not r["is_best"] + else "", + ), + # "Avg duration": + str(r["results_metrics"]["holding_avg"]), + # "Objective": + f"{r['loss']:,.5f}" if r["loss"] != 100000 else "N/A", + # "Max Drawdown (Acct)": + "{} {}".format( + fmt_coin( + r["results_metrics"]["max_drawdown_abs"], + stake_currency, + keep_trailing_zeros=True, + ), + (f"({r['results_metrics']['max_drawdown_account']:,.2%})").rjust(10, " "), + ) + if r["results_metrics"]["max_drawdown_account"] != 0.0 + else "--", + ], + style=" ".join( + [ + "bold gold1" if r["is_best"] and highlight_best else "", + "italic " if r["is_initial_point"] else "", + ] + ), + ) diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py index 50c55c43d..975338cd5 100644 --- a/freqtrade/optimize/hyperopt_tools.py +++ b/freqtrade/optimize/hyperopt_tools.py @@ -5,10 +5,7 @@ from pathlib import Path from typing import Any, Dict, Iterator, List, Optional, Tuple import numpy as np -import pandas as pd import rapidjson -import tabulate -from colorama import Fore, Style from pandas import isna, json_normalize from freqtrade.constants import FTHYPT_FILEVERSION, Config @@ -16,8 +13,6 @@ from freqtrade.enums import HyperoptState from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, round_dict, safe_value_fallback2 from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs -from freqtrade.optimize.optimize_reports import generate_wins_draws_losses -from freqtrade.util import fmt_coin logger = logging.getLogger(__name__) @@ -357,175 +352,6 @@ class HyperoptTools: + f"Objective: {results['loss']:.5f}" ) - @staticmethod - def prepare_trials_columns(trials: pd.DataFrame) -> pd.DataFrame: - trials["Best"] = "" - - if "results_metrics.winsdrawslosses" not in trials.columns: - # Ensure compatibility with older versions of hyperopt results - trials["results_metrics.winsdrawslosses"] = "N/A" - - has_account_drawdown = "results_metrics.max_drawdown_account" in trials.columns - if not has_account_drawdown: - # Ensure compatibility with older versions of hyperopt results - trials["results_metrics.max_drawdown_account"] = None - if "is_random" not in trials.columns: - trials["is_random"] = False - - # New mode, using backtest result for metrics - trials["results_metrics.winsdrawslosses"] = trials.apply( - lambda x: generate_wins_draws_losses( - x["results_metrics.wins"], x["results_metrics.draws"], x["results_metrics.losses"] - ), - axis=1, - ) - - trials = trials[ - [ - "Best", - "current_epoch", - "results_metrics.total_trades", - "results_metrics.winsdrawslosses", - "results_metrics.profit_mean", - "results_metrics.profit_total_abs", - "results_metrics.profit_total", - "results_metrics.holding_avg", - "results_metrics.max_drawdown_account", - "results_metrics.max_drawdown_abs", - "loss", - "is_initial_point", - "is_random", - "is_best", - ] - ] - - trials.columns = [ - "Best", - "Epoch", - "Trades", - " Win Draw Loss Win%", - "Avg profit", - "Total profit", - "Profit", - "Avg duration", - "max_drawdown_account", - "max_drawdown_abs", - "Objective", - "is_initial_point", - "is_random", - "is_best", - ] - - return trials - - @staticmethod - def get_result_table( - config: Config, - results: list, - total_epochs: int, - highlight_best: bool, - print_colorized: bool, - remove_header: int, - ) -> str: - """ - Log result table - """ - if not results: - return "" - - tabulate.PRESERVE_WHITESPACE = True - trials = json_normalize(results, max_level=1) - - trials = HyperoptTools.prepare_trials_columns(trials) - - trials["is_profit"] = False - trials.loc[trials["is_initial_point"] | trials["is_random"], "Best"] = "* " - trials.loc[trials["is_best"], "Best"] = "Best" - trials.loc[ - (trials["is_initial_point"] | trials["is_random"]) & trials["is_best"], "Best" - ] = "* Best" - trials.loc[trials["Total profit"] > 0, "is_profit"] = True - trials["Trades"] = trials["Trades"].astype(str) - # perc_multi = 1 if legacy_mode else 100 - trials["Epoch"] = trials["Epoch"].apply( - lambda x: "{}/{}".format(str(x).rjust(len(str(total_epochs)), " "), total_epochs) - ) - trials["Avg profit"] = trials["Avg profit"].apply( - lambda x: f"{x:,.2%}".rjust(7, " ") if not isna(x) else "--".rjust(7, " ") - ) - trials["Avg duration"] = trials["Avg duration"].apply( - lambda x: ( - f"{x:,.1f} m".rjust(7, " ") - if isinstance(x, float) - else f"{x}" - if not isna(x) - else "--".rjust(7, " ") - ) - ) - trials["Objective"] = trials["Objective"].apply( - lambda x: f"{x:,.5f}".rjust(8, " ") if x != 100000 else "N/A".rjust(8, " ") - ) - - stake_currency = config["stake_currency"] - - trials["Max Drawdown (Acct)"] = trials.apply( - lambda x: ( - "{} {}".format( - fmt_coin(x["max_drawdown_abs"], stake_currency, keep_trailing_zeros=True), - (f"({x['max_drawdown_account']:,.2%})").rjust(10, " "), - ).rjust(25 + len(stake_currency)) - if x["max_drawdown_account"] != 0.0 - else "--".rjust(25 + len(stake_currency)) - ), - axis=1, - ) - - trials = trials.drop(columns=["max_drawdown_abs", "max_drawdown_account"]) - - trials["Profit"] = trials.apply( - lambda x: ( - "{} {}".format( - fmt_coin(x["Total profit"], stake_currency, keep_trailing_zeros=True), - f"({x['Profit']:,.2%})".rjust(10, " "), - ).rjust(25 + len(stake_currency)) - if x["Total profit"] != 0.0 - else "--".rjust(25 + len(stake_currency)) - ), - axis=1, - ) - trials = trials.drop(columns=["Total profit"]) - - if print_colorized: - trials2 = trials.astype(str) - for i in range(len(trials)): - if trials.loc[i]["is_profit"]: - for j in range(len(trials.loc[i]) - 3): - trials2.iat[i, j] = f"{Fore.GREEN}{str(trials.iloc[i, j])}{Fore.RESET}" - if trials.loc[i]["is_best"] and highlight_best: - for j in range(len(trials.loc[i]) - 3): - trials2.iat[i, j] = ( - f"{Style.BRIGHT}{str(trials.iloc[i, j])}{Style.RESET_ALL}" - ) - trials = trials2 - del trials2 - trials = trials.drop(columns=["is_initial_point", "is_best", "is_profit", "is_random"]) - if remove_header > 0: - table = tabulate.tabulate( - trials.to_dict(orient="list"), tablefmt="orgtbl", headers="keys", stralign="right" - ) - - table = table.split("\n", remove_header)[remove_header] - elif remove_header < 0: - table = tabulate.tabulate( - trials.to_dict(orient="list"), tablefmt="psql", headers="keys", stralign="right" - ) - table = "\n".join(table.split("\n")[0:remove_header]) - else: - table = tabulate.tabulate( - trials.to_dict(orient="list"), tablefmt="psql", headers="keys", stralign="right" - ) - return table - @staticmethod def export_csv_file(config: Config, results: list, csv_file: str) -> None: """ diff --git a/freqtrade/optimize/optimize_reports/bt_output.py b/freqtrade/optimize/optimize_reports/bt_output.py index f20d7f190..620b6da7e 100644 --- a/freqtrade/optimize/optimize_reports/bt_output.py +++ b/freqtrade/optimize/optimize_reports/bt_output.py @@ -1,12 +1,10 @@ import logging -from typing import Any, Dict, List, Union - -from tabulate import tabulate +from typing import Any, Dict, List, Literal, Union from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config from freqtrade.optimize.optimize_reports.optimize_reports import generate_periodic_breakdown_stats from freqtrade.types import BacktestResultType -from freqtrade.util import decimals_per_coin, fmt_coin +from freqtrade.util import decimals_per_coin, fmt_coin, print_rich_table logger = logging.getLogger(__name__) @@ -46,22 +44,23 @@ def generate_wins_draws_losses(wins, draws, losses): return f"{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}" -def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str: +def text_table_bt_results( + pair_results: List[Dict[str, Any]], stake_currency: str, title: str +) -> None: """ Generates and returns a text table for the given backtest data and the results dataframe :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row :param stake_currency: stake-currency - used to correctly name headers - :return: pretty printed table with tabulate as string + :param title: Title of the table """ headers = _get_line_header("Pair", stake_currency, "Trades") - floatfmt = _get_line_floatfmt(stake_currency) output = [ [ t["key"], t["trades"], t["profit_mean_pct"], - t["profit_total_abs"], + f"{t['profit_total_abs']:.{decimals_per_coin(stake_currency)}f}", t["profit_total_pct"], t["duration_avg"], generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]), @@ -69,26 +68,32 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st for t in pair_results ] # Ignore type as floatfmt does allow tuples but mypy does not know that - return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") + print_rich_table(output, headers, summary=title) -def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str: +def text_table_tags( + tag_type: Literal["enter_tag", "exit_tag", "mix_tag"], + tag_results: List[Dict[str, Any]], + stake_currency: str, +) -> None: """ Generates and returns a text table for the given backtest data and the results dataframe :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row :param stake_currency: stake-currency - used to correctly name headers - :return: pretty printed table with tabulate as string """ floatfmt = _get_line_floatfmt(stake_currency) fallback: str = "" is_list = False if tag_type == "enter_tag": - headers = _get_line_header("Enter Tag", stake_currency, "Entries") + title = "Enter Tag" + headers = _get_line_header(title, stake_currency, "Entries") elif tag_type == "exit_tag": - headers = _get_line_header("Exit Reason", stake_currency, "Exits") + title = "Exit Reason" + headers = _get_line_header(title, stake_currency, "Exits") fallback = "exit_reason" else: # Mix tag + title = "Mixed Tag" headers = _get_line_header(["Enter Tag", "Exit Reason"], stake_currency, "Trades") floatfmt.insert(0, "s") is_list = True @@ -106,7 +111,7 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr ), t["trades"], t["profit_mean_pct"], - t["profit_total_abs"], + f"{t['profit_total_abs']:.{decimals_per_coin(stake_currency)}f}", t["profit_total_pct"], t.get("duration_avg"), generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]), @@ -114,17 +119,16 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr for t in tag_results ] # Ignore type as floatfmt does allow tuples but mypy does not know that - return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") + print_rich_table(output, headers, summary=f"{title.upper()} STATS") def text_table_periodic_breakdown( days_breakdown_stats: List[Dict[str, Any]], stake_currency: str, period: str -) -> str: +) -> None: """ Generate small table with Backtest results by days :param days_breakdown_stats: Days breakdown metrics :param stake_currency: Stakecurrency used - :return: pretty printed table with tabulate as string """ headers = [ period.capitalize(), @@ -143,17 +147,15 @@ def text_table_periodic_breakdown( ] for d in days_breakdown_stats ] - return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right") + print_rich_table(output, headers, summary=f"{period.upper()} BREAKDOWN") -def text_table_strategy(strategy_results, stake_currency: str) -> str: +def text_table_strategy(strategy_results, stake_currency: str, title: str): """ Generate summary table per strategy :param strategy_results: Dict of containing results for all strategies :param stake_currency: stake-currency - used to correctly name headers - :return: pretty printed table with tabulate as string """ - floatfmt = _get_line_floatfmt(stake_currency) headers = _get_line_header("Strategy", stake_currency, "Trades") # _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless # therefore we slip this column in only for strategy summary here. @@ -177,8 +179,8 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str: [ t["key"], t["trades"], - t["profit_mean_pct"], - t["profit_total_abs"], + f"{t['profit_mean_pct']:.2f}", + f"{t['profit_total_abs']:.{decimals_per_coin(stake_currency)}f}", t["profit_total_pct"], t["duration_avg"], generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]), @@ -186,11 +188,10 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str: ] for t, drawdown in zip(strategy_results, drawdown) ] - # Ignore type as floatfmt does allow tuples but mypy does not know that - return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") + print_rich_table(output, headers, summary=title) -def text_table_add_metrics(strat_results: Dict) -> str: +def text_table_add_metrics(strat_results: Dict) -> None: if len(strat_results["trades"]) > 0: best_trade = max(strat_results["trades"], key=lambda x: x["profit_ratio"]) worst_trade = min(strat_results["trades"], key=lambda x: x["profit_ratio"]) @@ -372,8 +373,8 @@ def text_table_add_metrics(strat_results: Dict) -> str: *drawdown_metrics, ("Market change", f"{strat_results['market_change']:.2%}"), ] + print_rich_table(metrics, ["Metric", "Value"], summary="SUMMARY METRICS", justify="left") - return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl") else: start_balance = fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"]) stake_amount = ( @@ -387,7 +388,7 @@ def text_table_add_metrics(strat_results: Dict) -> str: f"Your starting balance was {start_balance}, " f"and your stake was {stake_amount}." ) - return message + print(message) def _show_tag_subresults(results: Dict[str, Any], stake_currency: str): @@ -395,25 +396,13 @@ def _show_tag_subresults(results: Dict[str, Any], stake_currency: str): Print tag subresults (enter_tag, exit_reason_summary, mix_tag_stats) """ if (enter_tags := results.get("results_per_enter_tag")) is not None: - table = text_table_tags("enter_tag", enter_tags, stake_currency) - - if isinstance(table, str) and len(table) > 0: - print(" ENTER TAG STATS ".center(len(table.splitlines()[0]), "=")) - print(table) + text_table_tags("enter_tag", enter_tags, stake_currency) if (exit_reasons := results.get("exit_reason_summary")) is not None: - table = text_table_tags("exit_tag", exit_reasons, stake_currency) - - if isinstance(table, str) and len(table) > 0: - print(" EXIT REASON STATS ".center(len(table.splitlines()[0]), "=")) - print(table) + text_table_tags("exit_tag", exit_reasons, stake_currency) if (mix_tag := results.get("mix_tag_stats")) is not None: - table = text_table_tags("mix_tag", mix_tag, stake_currency) - - if isinstance(table, str) and len(table) > 0: - print(" MIXED TAG STATS ".center(len(table.splitlines()[0]), "=")) - print(table) + text_table_tags("mix_tag", mix_tag, stake_currency) def show_backtest_result( @@ -424,15 +413,12 @@ def show_backtest_result( """ # Print results print(f"Result for strategy {strategy}") - table = text_table_bt_results(results["results_per_pair"], stake_currency=stake_currency) - if isinstance(table, str): - print(" BACKTESTING REPORT ".center(len(table.splitlines()[0]), "=")) - print(table) - - table = text_table_bt_results(results["left_open_trades"], stake_currency=stake_currency) - if isinstance(table, str) and len(table) > 0: - print(" LEFT OPEN TRADES REPORT ".center(len(table.splitlines()[0]), "=")) - print(table) + text_table_bt_results( + results["results_per_pair"], stake_currency=stake_currency, title="BACKTESTING REPORT" + ) + text_table_bt_results( + results["left_open_trades"], stake_currency=stake_currency, title="LEFT OPEN TRADES REPORT" + ) _show_tag_subresults(results, stake_currency) @@ -443,20 +429,11 @@ def show_backtest_result( days_breakdown_stats = generate_periodic_breakdown_stats( trade_list=results["trades"], period=period ) - table = text_table_periodic_breakdown( + text_table_periodic_breakdown( days_breakdown_stats=days_breakdown_stats, stake_currency=stake_currency, period=period ) - if isinstance(table, str) and len(table) > 0: - print(f" {period.upper()} BREAKDOWN ".center(len(table.splitlines()[0]), "=")) - print(table) - table = text_table_add_metrics(results) - if isinstance(table, str) and len(table) > 0: - print(" SUMMARY METRICS ".center(len(table.splitlines()[0]), "=")) - print(table) - - if isinstance(table, str) and len(table) > 0: - print("=" * len(table.splitlines()[0])) + text_table_add_metrics(results) print() @@ -472,15 +449,13 @@ def show_backtest_results(config: Config, backtest_stats: BacktestResultType): if len(backtest_stats["strategy"]) > 0: # Print Strategy summary table - table = text_table_strategy(backtest_stats["strategy_comparison"], stake_currency) print( f"Backtested {results['backtest_start']} -> {results['backtest_end']} |" f" Max open trades : {results['max_open_trades']}" ) - print(" STRATEGY SUMMARY ".center(len(table.splitlines()[0]), "=")) - print(table) - print("=" * len(table.splitlines()[0])) - print("\nFor more details, please look at the detail tables above") + text_table_strategy( + backtest_stats["strategy_comparison"], stake_currency, "STRATEGY SUMMARY" + ) def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType): @@ -493,8 +468,7 @@ def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType): print("]") -def generate_edge_table(results: dict) -> str: - floatfmt = ("s", ".10g", ".2f", ".2f", ".2f", ".2f", "d", "d", "d") +def generate_edge_table(results: dict) -> None: tabular_data = [] headers = [ "Pair", @@ -512,17 +486,13 @@ def generate_edge_table(results: dict) -> str: tabular_data.append( [ result[0], - result[1].stoploss, - result[1].winrate, - result[1].risk_reward_ratio, - result[1].required_risk_reward, - result[1].expectancy, + f"{result[1].stoploss:.10g}", + f"{result[1].winrate:.2f}", + f"{result[1].risk_reward_ratio:.2f}", + f"{result[1].required_risk_reward:.2f}", + f"{result[1].expectancy:.2f}", result[1].nb_trades, round(result[1].avg_trade_duration), ] ) - - # Ignore type as floatfmt does allow tuples but mypy does not know that - return tabulate( - tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right" - ) + print_rich_table(tabular_data, headers, summary="EDGE TABLE") diff --git a/freqtrade/rpc/api_server/uvicorn_threaded.py b/freqtrade/rpc/api_server/uvicorn_threaded.py index cad8251db..d717c5567 100644 --- a/freqtrade/rpc/api_server/uvicorn_threaded.py +++ b/freqtrade/rpc/api_server/uvicorn_threaded.py @@ -1,4 +1,3 @@ -import contextlib import threading import time @@ -53,7 +52,6 @@ class UvicornServer(uvicorn.Server): loop = asyncio.new_event_loop() loop.run_until_complete(self.serve(sockets=sockets)) - @contextlib.contextmanager def run_in_thread(self): self.thread = threading.Thread(target=self.run, name="FTUvicorn") self.thread.start() diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 990ce4d82..8eaa970c8 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -1401,19 +1401,21 @@ class Telegram(RPCHandler): nrecent = int(context.args[0]) if context.args else 10 except (TypeError, ValueError, IndexError): nrecent = 10 + nonspot = self._config.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT trades = self._rpc._rpc_trade_history(nrecent) trades_tab = tabulate( [ [ dt_humanize_delta(dt_from_ts(trade["close_timestamp"])), - trade["pair"] + " (#" + str(trade["trade_id"]) + ")", + f"{trade['pair']} (#{trade['trade_id']}" + f"{(' ' + ('S' if trade['is_short'] else 'L')) if nonspot else ''})", f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})", ] for trade in trades["trades"] ], headers=[ "Close Date", - "Pair (ID)", + "Pair (ID L/S)" if nonspot else "Pair (ID)", f"Profit ({stake_cur})", ], tablefmt="simple", diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index e6d86926b..3893ee42b 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -5,6 +5,7 @@ This module defines the interface to apply for strategies import logging from abc import ABC, abstractmethod +from collections import OrderedDict from datetime import datetime, timedelta, timezone from math import isinf, isnan from typing import Dict, List, Optional, Tuple, Union @@ -12,6 +13,7 @@ from typing import Dict, List, Optional, Tuple, Union from pandas import DataFrame from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH, Config, IntOrInf, ListPairsWithTimeframes +from freqtrade.data.converter import populate_dataframe_with_trades from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import ( CandleType, @@ -139,6 +141,11 @@ class IStrategy(ABC, HyperStrategyMixin): # A self set parameter that represents the market direction. filled from configuration market_direction: MarketDirection = MarketDirection.NONE + # Global cache dictionary + _cached_grouped_trades_per_pair: Dict[ + str, OrderedDict[Tuple[datetime, datetime], DataFrame] + ] = {} + def __init__(self, config: Config) -> None: self.config = config # Dict to determine if analysis is necessary @@ -1040,6 +1047,7 @@ class IStrategy(ABC, HyperStrategyMixin): dataframe = self.advise_indicators(dataframe, metadata) dataframe = self.advise_entry(dataframe, metadata) dataframe = self.advise_exit(dataframe, metadata) + logger.debug("TA Analysis Ended") return dataframe def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame: @@ -1594,6 +1602,29 @@ class IStrategy(ABC, HyperStrategyMixin): dataframe = self.advise_exit(dataframe, metadata) return dataframe + def _if_enabled_populate_trades(self, dataframe: DataFrame, metadata: dict): + use_public_trades = self.config.get("exchange", {}).get("use_public_trades", False) + if use_public_trades: + trades = self.dp.trades(pair=metadata["pair"], copy=False) + + config = self.config + config["timeframe"] = self.timeframe + pair = metadata["pair"] + # TODO: slice trades to size of dataframe for faster backtesting + cached_grouped_trades: OrderedDict[Tuple[datetime, datetime], DataFrame] = ( + self._cached_grouped_trades_per_pair.get(pair, OrderedDict()) + ) + dataframe, cached_grouped_trades = populate_dataframe_with_trades( + cached_grouped_trades, config, dataframe, trades + ) + + # dereference old cache + if pair in self._cached_grouped_trades_per_pair: + del self._cached_grouped_trades_per_pair[pair] + self._cached_grouped_trades_per_pair[pair] = cached_grouped_trades + + logger.debug("Populated dataframe with trades.") + def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Populate indicators that will be used in the Buy, Sell, short, exit_short strategy @@ -1610,6 +1641,7 @@ class IStrategy(ABC, HyperStrategyMixin): self, dataframe, metadata, inf_data, populate_fn ) + self._if_enabled_populate_trades(dataframe, metadata) return self.populate_indicators(dataframe, metadata) def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame: diff --git a/freqtrade/util/__init__.py b/freqtrade/util/__init__.py index 503f5861a..8343790a0 100644 --- a/freqtrade/util/__init__.py +++ b/freqtrade/util/__init__.py @@ -15,6 +15,9 @@ from freqtrade.util.formatters import decimals_per_coin, fmt_coin, round_value from freqtrade.util.ft_precise import FtPrecise from freqtrade.util.measure_time import MeasureTime from freqtrade.util.periodic_cache import PeriodicCache +from freqtrade.util.progress_tracker import get_progress_tracker # noqa F401 +from freqtrade.util.rich_progress import CustomProgress +from freqtrade.util.rich_tables import print_df_rich_table, print_rich_table from freqtrade.util.template_renderer import render_template, render_template_with_fallback # noqa @@ -36,4 +39,7 @@ __all__ = [ "round_value", "fmt_coin", "MeasureTime", + "print_rich_table", + "print_df_rich_table", + "CustomProgress", ] diff --git a/freqtrade/util/progress_tracker.py b/freqtrade/util/progress_tracker.py new file mode 100644 index 000000000..ca8f5d3e9 --- /dev/null +++ b/freqtrade/util/progress_tracker.py @@ -0,0 +1,28 @@ +from rich.progress import ( + BarColumn, + MofNCompleteColumn, + TaskProgressColumn, + TextColumn, + TimeElapsedColumn, + TimeRemainingColumn, +) + +from freqtrade.util.rich_progress import CustomProgress + + +def get_progress_tracker(**kwargs): + """ + Get progress Bar with custom columns. + """ + return CustomProgress( + TextColumn("[progress.description]{task.description}"), + BarColumn(bar_width=None), + MofNCompleteColumn(), + TaskProgressColumn(), + "•", + TimeElapsedColumn(), + "•", + TimeRemainingColumn(), + expand=True, + **kwargs, + ) diff --git a/freqtrade/util/rich_progress.py b/freqtrade/util/rich_progress.py new file mode 100644 index 000000000..afa26683e --- /dev/null +++ b/freqtrade/util/rich_progress.py @@ -0,0 +1,14 @@ +from typing import Union + +from rich.console import ConsoleRenderable, Group, RichCast +from rich.progress import Progress + + +class CustomProgress(Progress): + def __init__(self, *args, cust_objs=[], **kwargs) -> None: + self._cust_objs = cust_objs + super().__init__(*args, **kwargs) + + def get_renderable(self) -> Union[ConsoleRenderable, RichCast, str]: + renderable = Group(*self._cust_objs, *self.get_renderables()) + return renderable diff --git a/freqtrade/util/rich_tables.py b/freqtrade/util/rich_tables.py new file mode 100644 index 000000000..d36bf9004 --- /dev/null +++ b/freqtrade/util/rich_tables.py @@ -0,0 +1,77 @@ +import sys +from typing import Any, Dict, List, Optional, Sequence, Union + +from pandas import DataFrame +from rich.console import Console +from rich.table import Column, Table +from rich.text import Text + + +TextOrString = Union[str, Text] + + +def print_rich_table( + tabular_data: Sequence[Union[Dict[str, Any], Sequence[TextOrString]]], + headers: Sequence[str], + summary: Optional[str] = None, + *, + justify="right", + table_kwargs: Optional[Dict[str, Any]] = None, +) -> None: + table = Table( + *[c if isinstance(c, Column) else Column(c, justify=justify) for c in headers], + title=summary, + **(table_kwargs or {}), + ) + + for row in tabular_data: + if isinstance(row, dict): + table.add_row( + *[ + row[header] if isinstance(row[header], Text) else str(row[header]) + for header in headers + ] + ) + + else: + row_to_add: List[Union[str, Text]] = [r if isinstance(r, Text) else str(r) for r in row] + table.add_row(*row_to_add) + + console = Console( + width=200 if "pytest" in sys.modules else None, + ) + console.print(table) + + +def _format_value(value: Any, *, floatfmt: str) -> str: + if isinstance(value, float): + return f"{value:{floatfmt}}" + return str(value) + + +def print_df_rich_table( + tabular_data: DataFrame, + headers: Sequence[str], + summary: Optional[str] = None, + *, + show_index=False, + index_name: Optional[str] = None, + table_kwargs: Optional[Dict[str, Any]] = None, +) -> None: + table = Table(title=summary, **(table_kwargs or {})) + + if show_index: + index_name = str(index_name) if index_name else tabular_data.index.name + table.add_column(index_name) + + for header in headers: + table.add_column(header, justify="right") + + for value_list in tabular_data.itertuples(index=show_index): + row = [_format_value(x, floatfmt=".3f") for x in value_list] + table.add_row(*row) + + console = Console( + width=200 if "pytest" in sys.modules else None, + ) + console.print(table) diff --git a/mkdocs.yml b/mkdocs.yml index 896dcd3f4..62274daa9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -48,6 +48,7 @@ nav: - Recursive analysis: recursive-analysis.md - Advanced Strategy: strategy-advanced.md - Advanced Hyperopt: advanced-hyperopt.md + - Orderflow: advanced-orderflow.md - Producer/Consumer mode: producer-consumer.md - SQL Cheat-sheet: sql_cheatsheet.md - Edge Positioning: edge.md diff --git a/requirements-dev.txt b/requirements-dev.txt index 8f529475b..a89604996 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -7,11 +7,11 @@ -r docs/requirements-docs.txt coveralls==4.0.1 -ruff==0.5.1 -mypy==1.10.1 +ruff==0.5.4 +mypy==1.11.0 pre-commit==3.7.1 -pytest==8.2.2 -pytest-asyncio==0.23.7 +pytest==8.3.1 +pytest-asyncio==0.23.8 pytest-cov==5.0.0 pytest-mock==3.14.0 pytest-random-order==1.1.1 @@ -25,8 +25,8 @@ time-machine==2.14.2 nbconvert==7.16.4 # mypy types -types-cachetools==5.3.0.7 +types-cachetools==5.4.0.20240717 types-filelock==3.2.7 -types-requests==2.32.0.20240622 +types-requests==2.32.0.20240712 types-tabulate==0.9.0.20240106 types-python-dateutil==2.9.0.20240316 diff --git a/requirements.txt b/requirements.txt index 994dc4d32..570459975 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,18 +4,18 @@ bottleneck==1.4.0 numexpr==2.10.1 pandas-ta==0.3.14b -ccxt==4.3.58 -cryptography==42.0.8 +ccxt==4.3.65 +cryptography==43.0.0 aiohttp==3.9.5 SQLAlchemy==2.0.31 -python-telegram-bot==21.3 +python-telegram-bot==21.4 # can't be hard-pinned due to telegram-bot pinning httpx with ~ httpx>=0.24.1 -humanize==4.9.0 -cachetools==5.3.3 +humanize==4.10.0 +cachetools==5.4.0 requests==2.32.3 urllib3==2.2.2 -jsonschema==4.22.0 +jsonschema==4.23.0 TA-Lib==0.4.32 technical==1.4.3 tabulate==0.9.0 @@ -24,7 +24,7 @@ jinja2==3.1.4 tables==3.9.1 joblib==1.4.2 rich==13.7.1 -pyarrow==16.1.0; platform_machine != 'armv7l' +pyarrow==17.0.0; platform_machine != 'armv7l' # find first, C search in arrays py_find_1st==1.1.6 @@ -38,15 +38,13 @@ orjson==3.10.6 sdnotify==0.3.2 # API Server -fastapi==0.111.0 +fastapi==0.111.1 pydantic==2.8.2 -uvicorn==0.30.1 +uvicorn==0.30.3 pyjwt==2.8.0 aiofiles==24.1.0 psutil==6.0.0 -# Support for colorized terminal output -colorama==0.4.6 # Building config files interactively questionary==2.0.1 prompt-toolkit==3.0.36 diff --git a/setup.py b/setup.py index 82e529767..6963862e0 100644 --- a/setup.py +++ b/setup.py @@ -88,7 +88,6 @@ setup( "py_find_1st", "python-rapidjson", "orjson", - "colorama", "jinja2", "questionary", "prompt-toolkit", diff --git a/tests/commands/test_commands.py b/tests/commands/test_commands.py index 02b234b6c..687bff69f 100644 --- a/tests/commands/test_commands.py +++ b/tests/commands/test_commands.py @@ -116,7 +116,7 @@ def test_list_exchanges(capsys): start_list_exchanges(get_args(args)) captured = capsys.readouterr() - assert re.match(r"Exchanges available for Freqtrade.*", captured.out) + assert re.search(r".*Exchanges available for Freqtrade.*", captured.out) assert re.search(r".*binance.*", captured.out) assert re.search(r".*bybit.*", captured.out) @@ -139,7 +139,7 @@ def test_list_exchanges(capsys): start_list_exchanges(get_args(args)) captured = capsys.readouterr() - assert re.match(r"All exchanges supported by the ccxt library.*", captured.out) + assert re.search(r"All exchanges supported by the ccxt library.*", captured.out) assert re.search(r".*binance.*", captured.out) assert re.search(r".*bingx.*", captured.out) assert re.search(r".*bitmex.*", captured.out) @@ -293,7 +293,7 @@ def test_list_markets(mocker, markets_static, capsys): pargs["config"] = None start_list_markets(pargs, False) captured = capsys.readouterr() - assert re.match("\nExchange Binance has 12 active markets:\n", captured.out) + assert re.search(r".*Exchange Binance has 12 active markets.*", captured.out) patch_exchange(mocker, api_mock=api_mock, exchange="binance", mock_markets=markets_static) # Test with --all: all markets @@ -491,7 +491,7 @@ def test_list_markets(mocker, markets_static, capsys): ] start_list_markets(get_args(args), False) captured = capsys.readouterr() - assert "Exchange Binance has 12 active markets:\n" in captured.out + assert "Exchange Binance has 12 active markets" in captured.out # Test tabular output, no markets found args = [ @@ -1633,8 +1633,8 @@ def test_start_list_data(testdatadir, capsys): start_list_data(pargs) captured = capsys.readouterr() assert "Found 16 pair / timeframe combinations." in captured.out - assert "\n| Pair | Timeframe | Type |\n" in captured.out - assert "\n| UNITTEST/BTC | 1m, 5m, 8m, 30m | spot |\n" in captured.out + assert re.search(r".*Pair.*Timeframe.*Type.*\n", captured.out) + assert re.search(r"\n.* UNITTEST/BTC .* 1m, 5m, 8m, 30m .* spot |\n", captured.out) args = [ "list-data", @@ -1650,9 +1650,9 @@ def test_start_list_data(testdatadir, capsys): start_list_data(pargs) captured = capsys.readouterr() assert "Found 2 pair / timeframe combinations." in captured.out - assert "\n| Pair | Timeframe | Type |\n" in captured.out + assert re.search(r".*Pair.*Timeframe.*Type.*\n", captured.out) assert "UNITTEST/BTC" not in captured.out - assert "\n| XRP/ETH | 1m, 5m | spot |\n" in captured.out + assert re.search(r"\n.* XRP/ETH .* 1m, 5m .* spot |\n", captured.out) args = [ "list-data", @@ -1667,9 +1667,9 @@ def test_start_list_data(testdatadir, capsys): captured = capsys.readouterr() assert "Found 6 pair / timeframe combinations." in captured.out - assert "\n| Pair | Timeframe | Type |\n" in captured.out - assert "\n| XRP/USDT:USDT | 5m, 1h | futures |\n" in captured.out - assert "\n| XRP/USDT:USDT | 1h, 8h | mark |\n" in captured.out + assert re.search(r".*Pair.*Timeframe.*Type.*\n", captured.out) + assert re.search(r"\n.* XRP/USDT:USDT .* 5m, 1h .* futures |\n", captured.out) + assert re.search(r"\n.* XRP/USDT:USDT .* 1h, 8h .* mark |\n", captured.out) args = [ "list-data", @@ -1684,15 +1684,12 @@ def test_start_list_data(testdatadir, capsys): start_list_data(pargs) captured = capsys.readouterr() assert "Found 2 pair / timeframe combinations." in captured.out - assert ( - "\n| Pair | Timeframe | Type " - "| From | To | Candles |\n" - ) in captured.out + assert re.search(r".*Pair.*Timeframe.*Type.*From .* To .* Candles .*\n", captured.out) assert "UNITTEST/BTC" not in captured.out - assert ( - "\n| XRP/ETH | 1m | spot | " - "2019-10-11 00:00:00 | 2019-10-13 11:19:00 | 2469 |\n" - ) in captured.out + assert re.search( + r"\n.* XRP/USDT .* 1m .* spot .* 2019-10-11 00:00:00 .* 2019-10-13 11:19:00 .* 2469 |\n", + captured.out, + ) @pytest.mark.usefixtures("init_persistence") diff --git a/tests/conftest.py b/tests/conftest.py index 22bc2556e..c0a9f2485 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -614,6 +614,7 @@ def get_default_conf(testdatadir): "internals": {}, "export": "none", "dataformat_ohlcv": "feather", + "dataformat_trades": "feather", "runmode": "dry_run", "candle_type_def": CandleType.SPOT, } diff --git a/tests/conftest_hyperopt.py b/tests/conftest_hyperopt.py index af4039a3c..315b138cf 100644 --- a/tests/conftest_hyperopt.py +++ b/tests/conftest_hyperopt.py @@ -324,7 +324,8 @@ def hyperopt_test_result(): "profit_mean": None, "profit_median": None, "profit_total": 0, - "profit": 0.0, + "max_drawdown_account": 0.0, + "max_drawdown_abs": 0.0, "holding_avg": timedelta(), }, # noqa: E501 "results_explanation": " 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.", # noqa: E501 diff --git a/tests/data/test_converter_orderflow.py b/tests/data/test_converter_orderflow.py new file mode 100644 index 000000000..9126edfe3 --- /dev/null +++ b/tests/data/test_converter_orderflow.py @@ -0,0 +1,483 @@ +from collections import OrderedDict + +import numpy as np +import pandas as pd +import pytest + +from freqtrade.constants import DEFAULT_TRADES_COLUMNS +from freqtrade.data.converter import populate_dataframe_with_trades +from freqtrade.data.converter.orderflow import trades_to_volumeprofile_with_total_delta_bid_ask +from freqtrade.data.converter.trade_converter import trades_list_to_df + + +BIN_SIZE_SCALE = 0.5 + + +def read_csv(filename, converter_columns: list = ["side", "type"]): + return pd.read_csv( + filename, + skipinitialspace=True, + index_col=0, + parse_dates=True, + date_format="ISO8601", + converters={col: str.strip for col in converter_columns}, + ) + + +@pytest.fixture +def populate_dataframe_with_trades_dataframe(testdatadir): + return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_DF.feather") + + +@pytest.fixture +def populate_dataframe_with_trades_trades(testdatadir): + return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_TRADES.feather") + + +@pytest.fixture +def candles(testdatadir): + return pd.read_json(testdatadir / "orderflow/candles.json").copy() + + +@pytest.fixture +def public_trades_list(testdatadir): + return read_csv(testdatadir / "orderflow/public_trades_list.csv").copy() + + +@pytest.fixture +def public_trades_list_simple(testdatadir): + return read_csv(testdatadir / "orderflow/public_trades_list_simple_example.csv").copy() + + +def test_public_trades_columns_before_change( + populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades +): + assert populate_dataframe_with_trades_dataframe.columns.tolist() == [ + "date", + "open", + "high", + "low", + "close", + "volume", + ] + assert populate_dataframe_with_trades_trades.columns.tolist() == [ + "timestamp", + "id", + "type", + "side", + "price", + "amount", + "cost", + "date", + ] + + +def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow( + populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades +): + """ + Tests the `populate_dataframe_with_trades` function's order flow calculation. + + This test checks the generated data frame and order flow for specific properties + based on the provided configuration and sample data. + """ + # Create copies of the input data to avoid modifying the originals + dataframe = populate_dataframe_with_trades_dataframe.copy() + trades = populate_dataframe_with_trades_trades.copy() + # Convert the 'date' column to datetime format with milliseconds + dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms") + # Select the last rows and reset the index (optional, depends on usage) + dataframe = dataframe.copy().tail().reset_index(drop=True) + # Define the configuration for order flow calculation + config = { + "timeframe": "5m", + "orderflow": { + "cache_size": 1000, + "max_candles": 1500, + "scale": 0.005, + "imbalance_volume": 0, + "imbalance_ratio": 3, + "stacked_imbalance_range": 3, + }, + } + # Apply the function to populate the data frame with order flow data + df, _ = populate_dataframe_with_trades(OrderedDict(), config, dataframe, trades) + # Extract results from the first row of the DataFrame + results = df.iloc[0] + t = results["trades"] + of = results["orderflow"] + + # Assert basic properties of the results + assert 0 != len(results) + assert 151 == len(t) + + # --- Order Flow Analysis --- + # Assert number of order flow data points + assert 23 == len(of) # Assert expected number of data points + + assert isinstance(of, dict) + + of_values = list(of.values()) + + # Assert specific order flow values at the beginning of the DataFrame + assert of_values[0] == { + "bid": 0.0, + "ask": 1.0, + "delta": 4.999, + "bid_amount": 0.0, + "ask_amount": 4.999, + "total_volume": 4.999, + "total_trades": 1, + } + + # Assert specific order flow values at the end of the DataFrame (excluding last row) + assert of_values[-1] == { + "bid": 0.0, + "ask": 1.0, + "delta": 0.103, + "bid_amount": 0.0, + "ask_amount": 0.103, + "total_volume": 0.103, + "total_trades": 1, + } + + # Extract order flow from the last row of the DataFrame + of = df.iloc[-1]["orderflow"] + + # Assert number of order flow data points in the last row + assert 19 == len(of) # Assert expected number of data points + + of_values1 = list(of.values()) + # Assert specific order flow values at the beginning of the last row + assert of_values1[0] == { + "bid": 1.0, + "ask": 0.0, + "delta": -12.536, + "bid_amount": 12.536, + "ask_amount": 0.0, + "total_volume": 12.536, + "total_trades": 1, + } + + # Assert specific order flow values at the end of the last row + assert pytest.approx(of_values1[-1]) == { + "bid": 4.0, + "ask": 3.0, + "delta": -40.948, + "bid_amount": 59.182, + "ask_amount": 18.23399, + "total_volume": 77.416, + "total_trades": 7, + } + + # --- Delta and Other Results --- + + # Assert delta value from the first row + assert pytest.approx(results["delta"]) == -50.519 + # Assert min and max delta values from the first row + assert results["min_delta"] == -79.469 + assert results["max_delta"] == 17.298 + + # Assert that stacked imbalances are NaN (not applicable in this test) + assert np.isnan(results["stacked_imbalances_bid"]) + assert np.isnan(results["stacked_imbalances_ask"]) + + # Repeat assertions for the third from last row + results = df.iloc[-2] + assert pytest.approx(results["delta"]) == -20.862 + assert pytest.approx(results["min_delta"]) == -54.559999 + assert 82.842 == results["max_delta"] + assert 234.99 == results["stacked_imbalances_bid"] + assert 234.96 == results["stacked_imbalances_ask"] + + # Repeat assertions for the last row + results = df.iloc[-1] + assert pytest.approx(results["delta"]) == -49.302 + assert results["min_delta"] == -70.222 + assert pytest.approx(results["max_delta"]) == 11.213 + assert np.isnan(results["stacked_imbalances_bid"]) + assert np.isnan(results["stacked_imbalances_ask"]) + + +def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades( + populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades +): + """ + Tests the `populate_dataframe_with_trades` function's handling of trades, + ensuring correct integration of trades data into the generated DataFrame. + """ + + # Create copies of the input data to avoid modifying the originals + dataframe = populate_dataframe_with_trades_dataframe.copy() + trades = populate_dataframe_with_trades_trades.copy() + + # --- Data Preparation --- + + # Convert the 'date' column to datetime format with milliseconds + dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms") + + # Select the final row of the DataFrame + dataframe = dataframe.tail().reset_index(drop=True) + + # Filter trades to those occurring after or at the same time as the first DataFrame date + trades = trades.loc[trades.date >= dataframe.date[0]] + trades.reset_index(inplace=True, drop=True) # Reset index for clarity + + # Assert the first trade ID to ensure filtering worked correctly + assert trades["id"][0] == "313881442" + + # --- Configuration and Function Call --- + + # Define configuration for order flow calculation (used for context) + config = { + "timeframe": "5m", + "orderflow": { + "cache_size": 1000, + "max_candles": 1500, + "scale": 0.5, + "imbalance_volume": 0, + "imbalance_ratio": 3, + "stacked_imbalance_range": 3, + }, + } + + # Populate the DataFrame with trades and order flow data + df, _ = populate_dataframe_with_trades(OrderedDict(), config, dataframe, trades) + + # --- DataFrame and Trade Data Validation --- + + row = df.iloc[0] # Extract the first row for assertions + + # Assert DataFrame structure + assert list(df.columns) == [ + # ... (list of expected column names) + "date", + "open", + "high", + "low", + "close", + "volume", + "trades", + "orderflow", + "imbalances", + "stacked_imbalances_bid", + "stacked_imbalances_ask", + "max_delta", + "min_delta", + "bid", + "ask", + "delta", + "total_trades", + ] + # Assert delta, bid, and ask values + assert pytest.approx(row["delta"]) == -50.519 + assert row["bid"] == 219.961 + assert row["ask"] == 169.442 + + # Assert the number of trades + assert len(row["trades"]) == 151 + + # Assert specific details of the first trade + t = row["trades"][0] + assert list(t.keys()) == ["timestamp", "id", "type", "side", "price", "amount", "cost", "date"] + assert trades["id"][0] == t["id"] + assert int(trades["timestamp"][0]) == int(t["timestamp"]) + assert t["side"] == "sell" + assert t["id"] == "313881442" + assert t["price"] == 234.72 + + +def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple, candles): + """ + Tests the integration of volume profile data into OHLCV candles. + + This test verifies that + the `trades_to_volumeprofile_with_total_delta_bid_ask` + function correctly calculates the volume profile and that + it correctly assigns the delta value from the volume profile to the + corresponding candle in the `candles` DataFrame. + """ + + # Convert the trade list to a DataFrame + trades_df = trades_list_to_df(public_trades_list_simple[DEFAULT_TRADES_COLUMNS].values.tolist()) + + # Generate the volume profile with the specified bin size + df = trades_to_volumeprofile_with_total_delta_bid_ask(trades_df, scale=BIN_SIZE_SCALE) + + # Assert the delta value in the total-bid/delta response of the second candle + assert 0.14 == df.values.tolist()[1][2] + + # Alternative assertion using `.iat` accessor (assuming correct assignment logic) + assert 0.14 == df["delta"].iat[1] + + +def test_public_trades_binned_big_sample_list(public_trades_list): + """ + Tests the `trades_to_volumeprofile_with_total_delta_bid_ask` function + with different bin sizes and verifies the generated DataFrame's structure and values. + """ + + # Define the bin size for the first test + BIN_SIZE_SCALE = 0.05 + + # Convert the trade list to a DataFrame + trades = trades_list_to_df(public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist()) + + # Generate the volume profile with the specified bin size + df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE) + + # Assert that the DataFrame has the expected columns + assert df.columns.tolist() == [ + "bid", + "ask", + "delta", + "bid_amount", + "ask_amount", + "total_volume", + "total_trades", + ] + + # Assert the number of rows in the DataFrame (expected 23 for this bin size) + assert len(df) == 23 + + # Assert that the index values are in ascending order and spaced correctly + assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1)) + assert df.index[0] + BIN_SIZE_SCALE == df.index[1] + assert (trades["price"].min() - BIN_SIZE_SCALE) < df.index[0] < trades["price"].max() + assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1] + assert (trades["price"].max() - BIN_SIZE_SCALE) < df.index[-1] < trades["price"].max() + + # Assert specific values in the first and last rows of the DataFrame + assert 32 == df["bid"].iloc[0] # bid price + assert 197.512 == df["bid_amount"].iloc[0] # total bid amount + assert 88.98 == df["ask_amount"].iloc[0] # total ask amount + assert 26 == df["ask"].iloc[0] # ask price + assert -108.532 == pytest.approx(df["delta"].iloc[0]) # delta (bid amount - ask amount) + + assert 3 == df["bid"].iloc[-1] # bid price + assert 50.659 == df["bid_amount"].iloc[-1] # total bid amount + assert 108.21 == df["ask_amount"].iloc[-1] # total ask amount + assert 44 == df["ask"].iloc[-1] # ask price + assert 57.551 == df["delta"].iloc[-1] # delta (bid amount - ask amount) + + # Repeat the process with a larger bin size + BIN_SIZE_SCALE = 1 + + # Generate the volume profile with the larger bin size + df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE) + + # Assert the number of rows in the DataFrame (expected 2 for this bin size) + assert len(df) == 2 + + # Repeat similar assertions for index ordering and spacing + assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1)) + assert (trades["price"].min() - BIN_SIZE_SCALE) < df.index[0] < trades["price"].max() + assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1] + assert (trades["price"].max() - BIN_SIZE_SCALE) < df.index[-1] < trades["price"].max() + + # Assert the value in the last row of the DataFrame with the larger bin size + assert 1667.0 == df.index[-1] + assert 710.98 == df["bid_amount"].iat[0] + assert 111 == df["bid"].iat[0] + assert 52.7199999 == pytest.approx(df["delta"].iat[0]) # delta + + +def test_public_trades_config_max_trades( + default_conf, populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades +): + dataframe = populate_dataframe_with_trades_dataframe.copy() + trades = populate_dataframe_with_trades_trades.copy() + default_conf["exchange"]["use_public_trades"] = True + orderflow_config = { + "timeframe": "5m", + "orderflow": { + "cache_size": 1000, + "max_candles": 1, + "scale": 0.005, + "imbalance_volume": 0, + "imbalance_ratio": 3, + "stacked_imbalance_range": 3, + }, + } + + df, _ = populate_dataframe_with_trades( + OrderedDict(), default_conf | orderflow_config, dataframe, trades + ) + assert df.delta.count() == 1 + + +def test_public_trades_testdata_sanity( + candles, + public_trades_list, + public_trades_list_simple, + populate_dataframe_with_trades_dataframe, + populate_dataframe_with_trades_trades, +): + assert 10999 == len(candles) + assert 1000 == len(public_trades_list) + assert 999 == len(populate_dataframe_with_trades_dataframe) + assert 293532 == len(populate_dataframe_with_trades_trades) + + assert 7 == len(public_trades_list_simple) + assert ( + 5 + == public_trades_list_simple.loc[ + public_trades_list_simple["side"].str.contains("sell"), "id" + ].count() + ) + assert ( + 2 + == public_trades_list_simple.loc[ + public_trades_list_simple["side"].str.contains("buy"), "id" + ].count() + ) + + assert public_trades_list.columns.tolist() == [ + "timestamp", + "id", + "type", + "side", + "price", + "amount", + "cost", + "date", + ] + + assert public_trades_list.columns.tolist() == [ + "timestamp", + "id", + "type", + "side", + "price", + "amount", + "cost", + "date", + ] + assert public_trades_list_simple.columns.tolist() == [ + "timestamp", + "id", + "type", + "side", + "price", + "amount", + "cost", + "date", + ] + assert populate_dataframe_with_trades_dataframe.columns.tolist() == [ + "date", + "open", + "high", + "low", + "close", + "volume", + ] + assert populate_dataframe_with_trades_trades.columns.tolist() == [ + "timestamp", + "id", + "type", + "side", + "price", + "amount", + "cost", + "date", + ] diff --git a/tests/data/test_dataprovider.py b/tests/data/test_dataprovider.py index e7bd62df5..8656da10b 100644 --- a/tests/data/test_dataprovider.py +++ b/tests/data/test_dataprovider.py @@ -62,6 +62,42 @@ def test_historic_ohlcv(mocker, default_conf, ohlcv_history): assert historymock.call_args_list[0][1]["timeframe"] == "5m" +def test_historic_trades(mocker, default_conf, trades_history_df): + historymock = MagicMock(return_value=trades_history_df) + mocker.patch( + "freqtrade.data.history.datahandlers.featherdatahandler.FeatherDataHandler._trades_load", + historymock, + ) + + dp = DataProvider(default_conf, None) + # Live mode.. + with pytest.raises(OperationalException, match=r"Exchange is not available to DataProvider\."): + dp.trades("UNITTEST/BTC", "5m") + + exchange = get_patched_exchange(mocker, default_conf) + dp = DataProvider(default_conf, exchange) + data = dp.trades("UNITTEST/BTC", "5m") + + assert isinstance(data, DataFrame) + assert len(data) == 0 + + # Switch to backtest mode + default_conf["runmode"] = RunMode.BACKTEST + default_conf["dataformat_trades"] = "feather" + exchange = get_patched_exchange(mocker, default_conf) + dp = DataProvider(default_conf, exchange) + data = dp.trades("UNITTEST/BTC", "5m") + assert isinstance(data, DataFrame) + assert len(data) == len(trades_history_df) + + # Random other runmode + default_conf["runmode"] = RunMode.UTIL_EXCHANGE + dp = DataProvider(default_conf, None) + data = dp.trades("UNITTEST/BTC", "5m") + assert isinstance(data, DataFrame) + assert len(data) == 0 + + def test_historic_ohlcv_dataformat(mocker, default_conf, ohlcv_history): hdf5loadmock = MagicMock(return_value=ohlcv_history) featherloadmock = MagicMock(return_value=ohlcv_history) @@ -247,8 +283,8 @@ def test_emit_df(mocker, default_conf, ohlcv_history): def test_refresh(mocker, default_conf): - refresh_mock = MagicMock() - mocker.patch(f"{EXMS}.refresh_latest_ohlcv", refresh_mock) + refresh_mock = mocker.patch(f"{EXMS}.refresh_latest_ohlcv") + mock_refresh_trades = mocker.patch(f"{EXMS}.refresh_latest_trades") exchange = get_patched_exchange(mocker, default_conf, exchange="binance") timeframe = default_conf["timeframe"] @@ -258,7 +294,7 @@ def test_refresh(mocker, default_conf): dp = DataProvider(default_conf, exchange) dp.refresh(pairs) - + assert mock_refresh_trades.call_count == 0 assert refresh_mock.call_count == 1 assert len(refresh_mock.call_args[0]) == 1 assert len(refresh_mock.call_args[0][0]) == len(pairs) @@ -266,11 +302,20 @@ def test_refresh(mocker, default_conf): refresh_mock.reset_mock() dp.refresh(pairs, pairs_non_trad) + assert mock_refresh_trades.call_count == 0 assert refresh_mock.call_count == 1 assert len(refresh_mock.call_args[0]) == 1 assert len(refresh_mock.call_args[0][0]) == len(pairs) + len(pairs_non_trad) assert refresh_mock.call_args[0][0] == pairs + pairs_non_trad + # Test with public trades + refresh_mock.reset_mock() + refresh_mock.reset_mock() + default_conf["exchange"]["use_public_trades"] = True + dp.refresh(pairs, pairs_non_trad) + assert mock_refresh_trades.call_count == 1 + assert refresh_mock.call_count == 1 + def test_orderbook(mocker, default_conf, order_book_l2): api_mock = MagicMock() diff --git a/tests/data/test_entryexitanalysis.py b/tests/data/test_entryexitanalysis.py index 1a5309190..e7909c339 100644 --- a/tests/data/test_entryexitanalysis.py +++ b/tests/data/test_entryexitanalysis.py @@ -154,10 +154,10 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, use assert "-3.5" in captured.out assert "50" in captured.out assert "0" in captured.out - assert "0.01616" in captured.out + assert "0.016" in captured.out assert "34.049" in captured.out - assert "0.104411" in captured.out - assert "52.8292" in captured.out + assert "0.104" in captured.out + assert "52.829" in captured.out # test group 1 args = get_args(base_args + ["--analysis-groups", "1"]) diff --git a/tests/data/test_history.py b/tests/data/test_history.py index e3f92b1f9..425b620f8 100644 --- a/tests/data/test_history.py +++ b/tests/data/test_history.py @@ -151,9 +151,7 @@ def test_load_data_with_new_pair_1min( ) load_pair_history(datadir=tmp_path, timeframe="1m", pair="MEME/BTC", candle_type=candle_type) assert file.is_file() - assert log_has_re( - r'\(0/1\) - Download history data for "MEME/BTC", 1m, ' r"spot and store in .*", caplog - ) + assert log_has_re(r'Download history data for "MEME/BTC", 1m, ' r"spot and store in .*", caplog) def test_testdata_path(testdatadir) -> None: @@ -677,7 +675,7 @@ def test_download_trades_history( assert not _download_trades_history( data_handler=data_handler, exchange=exchange, pair="ETH/BTC", trading_mode=TradingMode.SPOT ) - assert log_has_re('Failed to download historic trades for pair: "ETH/BTC".*', caplog) + assert log_has_re('Failed to download and store historic trades for pair: "ETH/BTC".*', caplog) file2 = tmp_path / "XRP_ETH-trades.json.gz" copyfile(testdatadir / file2.name, file2) diff --git a/tests/exchange/test_binance.py b/tests/exchange/test_binance.py index 1de464097..a5f9ea654 100644 --- a/tests/exchange/test_binance.py +++ b/tests/exchange/test_binance.py @@ -600,7 +600,7 @@ async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog, c @pytest.mark.parametrize( - "pair,nominal_value,mm_ratio,amt", + "pair,notional_value,mm_ratio,amt", [ ("XRP/USDT:USDT", 0.0, 0.025, 0), ("BNB/USDT:USDT", 100.0, 0.0065, 0), @@ -615,12 +615,12 @@ def test_get_maintenance_ratio_and_amt_binance( mocker, leverage_tiers, pair, - nominal_value, + notional_value, mm_ratio, amt, ): mocker.patch(f"{EXMS}.exchange_has", return_value=True) exchange = get_patched_exchange(mocker, default_conf, exchange="binance") exchange._leverage_tiers = leverage_tiers - (result_ratio, result_amt) = exchange.get_maintenance_ratio_and_amt(pair, nominal_value) + (result_ratio, result_amt) = exchange.get_maintenance_ratio_and_amt(pair, notional_value) assert (round(result_ratio, 8), round(result_amt, 8)) == (mm_ratio, amt) diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index 423c9b58e..168157cea 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -8,8 +8,9 @@ from unittest.mock import MagicMock, Mock, PropertyMock, patch import ccxt import pytest from numpy import nan -from pandas import DataFrame +from pandas import DataFrame, to_datetime +from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS from freqtrade.enums import CandleType, MarginMode, RunMode, TradingMode from freqtrade.exceptions import ( ConfigurationError, @@ -325,6 +326,22 @@ def test_validate_order_time_in_force(default_conf, mocker, caplog): ex.validate_order_time_in_force(tif2) +def test_validate_orderflow(default_conf, mocker, caplog): + caplog.set_level(logging.INFO) + # Test bybit - as it doesn't support historic trades data. + ex = get_patched_exchange(mocker, default_conf, exchange="bybit") + mocker.patch(f"{EXMS}.exchange_has", return_value=True) + ex.validate_orderflow({"use_public_trades": False}) + + with pytest.raises(ConfigurationError, match=r"Trade data not available for.*"): + ex.validate_orderflow({"use_public_trades": True}) + + # Binance supports orderflow. + ex = get_patched_exchange(mocker, default_conf, exchange="binance") + ex.validate_orderflow({"use_public_trades": False}) + ex.validate_orderflow({"use_public_trades": True}) + + @pytest.mark.parametrize( "price,precision_mode,precision,expected", [ @@ -2371,6 +2388,163 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog, candle_type) -> None assert len(res) == 1 +@pytest.mark.parametrize("candle_type", [CandleType.FUTURES, CandleType.SPOT]) +def test_refresh_latest_trades( + mocker, default_conf, caplog, candle_type, tmp_path, time_machine +) -> None: + time_machine.move_to(dt_now(), tick=False) + trades = [ + { + # unix timestamp ms + "timestamp": dt_ts(dt_now() - timedelta(minutes=5)), + "amount": 16.512, + "cost": 10134.07488, + "fee": None, + "fees": [], + "id": "354669639", + "order": None, + "price": 613.74, + "side": "sell", + "takerOrMaker": None, + "type": None, + }, + { + "timestamp": dt_ts(), # unix timestamp ms + "amount": 12.512, + "cost": 1000, + "fee": None, + "fees": [], + "id": "354669640", + "order": None, + "price": 613.84, + "side": "buy", + "takerOrMaker": None, + "type": None, + }, + ] + + caplog.set_level(logging.DEBUG) + use_trades_conf = default_conf + use_trades_conf["exchange"]["use_public_trades"] = True + use_trades_conf["datadir"] = tmp_path + use_trades_conf["orderflow"] = {"max_candles": 1500} + exchange = get_patched_exchange(mocker, use_trades_conf) + exchange._api_async.fetch_trades = get_mock_coro(trades) + exchange._ft_has["exchange_has_overrides"]["fetchTrades"] = True + + pairs = [("IOTA/USDT:USDT", "5m", candle_type), ("XRP/USDT:USDT", "5m", candle_type)] + # empty dicts + assert not exchange._trades + res = exchange.refresh_latest_trades(pairs, cache=False) + # No caching + assert not exchange._trades + + assert len(res) == len(pairs) + assert exchange._api_async.fetch_trades.call_count == 4 + exchange._api_async.fetch_trades.reset_mock() + + exchange.required_candle_call_count = 2 + res = exchange.refresh_latest_trades(pairs) + assert len(res) == len(pairs) + + assert log_has(f"Refreshing TRADES data for {len(pairs)} pairs", caplog) + assert exchange._trades + assert exchange._api_async.fetch_trades.call_count == 4 + exchange._api_async.fetch_trades.reset_mock() + for pair in pairs: + assert isinstance(exchange.trades(pair), DataFrame) + assert len(exchange.trades(pair)) > 0 + + # trades function should return a different object on each call + # if copy is "True" + assert exchange.trades(pair) is not exchange.trades(pair) + assert exchange.trades(pair) is not exchange.trades(pair, copy=True) + assert exchange.trades(pair, copy=True) is not exchange.trades(pair, copy=True) + assert exchange.trades(pair, copy=False) is exchange.trades(pair, copy=False) + + # test caching + ohlcv = [ + [ + dt_ts(dt_now() - timedelta(minutes=5)), # unix timestamp ms + 1, # open + 2, # high + 3, # low + 4, # close + 5, # volume (in quote currency) + ], + [ + dt_ts(), # unix timestamp ms + 3, # open + 1, # high + 4, # low + 6, # close + 5, # volume (in quote currency) + ], + ] + cols = DEFAULT_DATAFRAME_COLUMNS + trades_df = DataFrame(ohlcv, columns=cols) + + trades_df["date"] = to_datetime(trades_df["date"], unit="ms", utc=True) + trades_df["date"] = trades_df["date"].apply(lambda date: timeframe_to_prev_date("5m", date)) + exchange._klines[pair] = trades_df + res = exchange.refresh_latest_trades( + [("IOTA/USDT:USDT", "5m", candle_type), ("XRP/USDT:USDT", "5m", candle_type)] + ) + assert len(res) == 0 + assert exchange._api_async.fetch_trades.call_count == 0 + caplog.clear() + + # Reset refresh times + for pair in pairs: + # test caching with "expired" candle + trades = [ + { + # unix timestamp ms + "timestamp": dt_ts(exchange._klines[pair].iloc[-1].date - timedelta(minutes=5)), + "amount": 16.512, + "cost": 10134.07488, + "fee": None, + "fees": [], + "id": "354669639", + "order": None, + "price": 613.74, + "side": "sell", + "takerOrMaker": None, + "type": None, + } + ] + trades_df = DataFrame(trades) + trades_df["date"] = to_datetime(trades_df["timestamp"], unit="ms", utc=True) + exchange._trades[pair] = trades_df + res = exchange.refresh_latest_trades( + [("IOTA/USDT:USDT", "5m", candle_type), ("XRP/USDT:USDT", "5m", candle_type)] + ) + assert len(res) == len(pairs) + + assert exchange._api_async.fetch_trades.call_count == 4 + + # cache - but disabled caching + exchange._api_async.fetch_trades.reset_mock() + exchange.required_candle_call_count = 1 + + pairlist = [ + ("IOTA/ETH", "5m", candle_type), + ("XRP/ETH", "5m", candle_type), + ("XRP/ETH", "1d", candle_type), + ] + res = exchange.refresh_latest_trades(pairlist, cache=False) + assert len(res) == 3 + assert exchange._api_async.fetch_trades.call_count == 6 + + # Test the same again, should NOT return from cache! + exchange._api_async.fetch_trades.reset_mock() + res = exchange.refresh_latest_trades(pairlist, cache=False) + assert len(res) == 3 + assert exchange._api_async.fetch_trades.call_count == 6 + exchange._api_async.fetch_trades.reset_mock() + caplog.clear() + + @pytest.mark.parametrize("candle_type", [CandleType.FUTURES, CandleType.MARK, CandleType.SPOT]) def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_machine) -> None: start = datetime(2021, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc) diff --git a/tests/optimize/test_hyperopt.py b/tests/optimize/test_hyperopt.py index 2e9ae8d35..a9f697629 100644 --- a/tests/optimize/test_hyperopt.py +++ b/tests/optimize/test_hyperopt.py @@ -291,9 +291,10 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None: "is_best": True, } ) + hyperopt._hyper_out.print() out, _err = capsys.readouterr() assert all( - x in out for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC (1.00%)", "00:20:00"] + x in out for x in ["Best", "2/2", "1", "0.10%", "0.00100000 BTC (1.00%)", "0:20:00"] ) diff --git a/tests/optimize/test_lookahead_analysis.py b/tests/optimize/test_lookahead_analysis.py index 88e3ad877..f7d38b24b 100644 --- a/tests/optimize/test_lookahead_analysis.py +++ b/tests/optimize/test_lookahead_analysis.py @@ -147,7 +147,7 @@ def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf instance = LookaheadAnalysis(lookahead_conf, strategy_obj) instance.current_analysis = analysis - _table, _headers, data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( + data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( lookahead_conf, [instance] ) @@ -163,14 +163,14 @@ def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf analysis.false_exit_signals = 10 instance = LookaheadAnalysis(lookahead_conf, strategy_obj) instance.current_analysis = analysis - _table, _headers, data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( + data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( lookahead_conf, [instance] ) assert data[0][2].__contains__("error") # edit it into not showing an error instance.failed_bias_check = False - _table, _headers, data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( + data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( lookahead_conf, [instance] ) assert data[0][0] == "strategy_test_v3_with_lookahead_bias.py" @@ -183,7 +183,7 @@ def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf analysis.false_indicators.append("falseIndicator1") analysis.false_indicators.append("falseIndicator2") - _table, _headers, data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( + data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( lookahead_conf, [instance] ) @@ -193,7 +193,7 @@ def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf assert len(data) == 1 # check amount of multiple rows - _table, _headers, data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( + data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( lookahead_conf, [instance, instance, instance] ) assert len(data) == 3 diff --git a/tests/optimize/test_optimize_reports.py b/tests/optimize/test_optimize_reports.py index 9e141e22d..4c7ce06e8 100644 --- a/tests/optimize/test_optimize_reports.py +++ b/tests/optimize/test_optimize_reports.py @@ -59,7 +59,7 @@ def _backup_file(file: Path, copy_file: bool = False) -> None: copyfile(file_swp, file) -def test_text_table_bt_results(): +def test_text_table_bt_results(capsys): results = pd.DataFrame( { "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"], @@ -72,7 +72,8 @@ def test_text_table_bt_results(): pair_results = generate_pair_metrics( ["ETH/BTC"], stake_currency="BTC", starting_balance=4, results=results ) - text = text_table_bt_results(pair_results, stake_currency="BTC") + text_table_bt_results(pair_results, stake_currency="BTC", title="title") + text = capsys.readouterr().out re.search( r".* Pair .* Trades .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* " r"Avg Duration .* Win Draw Loss Win% .*", @@ -435,7 +436,7 @@ def test_calc_streak(testdatadir): assert calc_streak(bt_data) == (7, 18) -def test_text_table_exit_reason(): +def test_text_table_exit_reason(capsys): results = pd.DataFrame( { "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"], @@ -452,7 +453,8 @@ def test_text_table_exit_reason(): exit_reason_stats = generate_tag_metrics( "exit_reason", starting_balance=22, results=results, skip_nan=False ) - text = text_table_tags("exit_tag", exit_reason_stats, "BTC") + text_table_tags("exit_tag", exit_reason_stats, "BTC") + text = capsys.readouterr().out assert re.search( r".* Exit Reason .* Exits .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* " @@ -460,11 +462,11 @@ def test_text_table_exit_reason(): text, ) assert re.search( - r".* roi .* 2 .* 15.00 .* 0.60000000 .* 2.73 .* 0:20:00 .* 2 0 0 100 .*", + r".* roi .* 2 .* 15.0 .* 0.60000000 .* 2.73 .* 0:20:00 .* 2 0 0 100 .*", text, ) assert re.search( - r".* stop_loss .* 1 .* -10.00 .* -0.20000000 .* -0.91 .* 0:10:00 .* 0 0 1 0 .*", + r".* stop_loss .* 1 .* -10.0 .* -0.20000000 .* -0.91 .* 0:10:00 .* 0 0 1 0 .*", text, ) assert re.search( @@ -507,7 +509,7 @@ def test_generate_sell_reason_stats(): assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2) -def test_text_table_strategy(testdatadir): +def test_text_table_strategy(testdatadir, capsys): filename = testdatadir / "backtest_results/backtest-result_multistrat.json" bt_res_data = load_backtest_stats(filename) @@ -515,8 +517,10 @@ def test_text_table_strategy(testdatadir): strategy_results = generate_strategy_comparison(bt_stats=bt_res_data["strategy"]) assert strategy_results == bt_res_data_comparison - text = text_table_strategy(strategy_results, "BTC") + text_table_strategy(strategy_results, "BTC", "STRATEGY SUMMARY") + captured = capsys.readouterr() + text = captured.out assert re.search( r".* Strategy .* Trades .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* " r"Avg Duration .* Win Draw Loss Win% .* Drawdown .*", @@ -534,12 +538,12 @@ def test_text_table_strategy(testdatadir): ) -def test_generate_edge_table(): +def test_generate_edge_table(capsys): results = {} results["ETH/BTC"] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60) - text = generate_edge_table(results) - assert text.count("+") == 7 - assert text.count("| ETH/BTC |") == 1 + generate_edge_table(results) + text = capsys.readouterr().out + assert re.search(r".* ETH/BTC .*", text) assert re.search(r".* Risk Reward Ratio .* Required Risk Reward .* Expectancy .*", text) diff --git a/tests/optimize/test_recursive_analysis.py b/tests/optimize/test_recursive_analysis.py index 2969b4153..e16c82d24 100644 --- a/tests/optimize/test_recursive_analysis.py +++ b/tests/optimize/test_recursive_analysis.py @@ -105,9 +105,7 @@ def test_recursive_helper_text_table_recursive_analysis_instances(recursive_conf instance = RecursiveAnalysis(recursive_conf, strategy_obj) instance.dict_recursive = dict_diff - _table, _headers, data = RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances( - [instance] - ) + data = RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances([instance]) # check row contents for a try that has too few signals assert data[0][0] == "rsi" @@ -118,9 +116,7 @@ def test_recursive_helper_text_table_recursive_analysis_instances(recursive_conf dict_diff = dict() instance = RecursiveAnalysis(recursive_conf, strategy_obj) instance.dict_recursive = dict_diff - _table, _headers, data = RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances( - [instance] - ) + data = RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances([instance]) assert len(data) == 0 diff --git a/tests/test_configuration.py b/tests/test_configuration.py index f9368246a..e160130c7 100644 --- a/tests/test_configuration.py +++ b/tests/test_configuration.py @@ -27,7 +27,7 @@ from freqtrade.configuration.load_config import ( ) from freqtrade.constants import DEFAULT_DB_DRYRUN_URL, DEFAULT_DB_PROD_URL, ENV_VAR_PREFIX from freqtrade.enums import RunMode -from freqtrade.exceptions import OperationalException +from freqtrade.exceptions import ConfigurationError, OperationalException from tests.conftest import ( CURRENT_TEST_STRATEGY, log_has, @@ -1084,6 +1084,29 @@ def test__validate_consumers(default_conf, caplog) -> None: assert log_has_re("To receive best performance with external data.*", caplog) +def test__validate_orderflow(default_conf) -> None: + conf = deepcopy(default_conf) + conf["exchange"]["use_public_trades"] = True + with pytest.raises( + ConfigurationError, + match="Orderflow is a required configuration key when using public trades.", + ): + validate_config_consistency(conf) + + conf.update( + { + "orderflow": { + "scale": 0.5, + "stacked_imbalance_range": 3, + "imbalance_volume": 100, + "imbalance_ratio": 3, + } + } + ) + # Should pass. + validate_config_consistency(conf) + + def test_load_config_test_comments() -> None: """ Load config with comments diff --git a/tests/testdata/orderflow/candles.json b/tests/testdata/orderflow/candles.json new file mode 100644 index 000000000..0e7fd997c --- /dev/null +++ b/tests/testdata/orderflow/candles.json @@ -0,0 +1 @@ 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tests/util/test_datetime_helpers.py diff --git a/tests/utils/test_formatters.py b/tests/util/test_formatters.py similarity index 100% rename from tests/utils/test_formatters.py rename to tests/util/test_formatters.py diff --git a/tests/utils/test_funding_rate_migration.py b/tests/util/test_funding_rate_migration.py similarity index 100% rename from tests/utils/test_funding_rate_migration.py rename to tests/util/test_funding_rate_migration.py diff --git a/tests/utils/test_measure_time.py b/tests/util/test_measure_time.py similarity index 100% rename from tests/utils/test_measure_time.py rename to tests/util/test_measure_time.py diff --git a/tests/utils/test_periodiccache.py b/tests/util/test_periodiccache.py similarity index 100% rename from tests/utils/test_periodiccache.py rename to tests/util/test_periodiccache.py diff --git a/tests/utils/test_rendering_utils.py b/tests/util/test_rendering_utils.py similarity index 100% rename from tests/utils/test_rendering_utils.py rename to tests/util/test_rendering_utils.py