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https://github.com/freqtrade/freqtrade.git
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improve example strat so that it has dynamic buy and sell logic according to original prediction
This commit is contained in:
robcaulk
parent
4eb29c8810
commit
ce365eb9e3
3
.gitignore
vendored
3
.gitignore
vendored
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@ -109,4 +109,5 @@ target/
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!config_examples/config_ftx.example.json
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!config_examples/config_full.example.json
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!config_examples/config_kraken.example.json
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!config_examples/config_freqai.example.json
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!config_examples/config_freqai_futures.example.json
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!config_examples/config_freqai_spot.example.json
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93
config_examples/config_freqai_futures.example.json
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93
config_examples/config_freqai_futures.example.json
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@ -0,0 +1,93 @@
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{
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"trading_mode": "futures",
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"margin_mode": "isolated",
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"max_open_trades": 5,
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"stake_currency": "USDT",
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"stake_amount": 200,
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"tradable_balance_ratio": 1,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"timeframe": "3m",
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"dry_run_wallet": 1000,
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"cancel_open_orders_on_exit": true,
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"unfilledtimeout": {
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"entry": 10,
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"exit": 30
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},
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"exchange": {
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"name": "okx",
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"key": "",
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"secret": "",
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"ccxt_config": {
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"enableRateLimit": true
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},
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"ccxt_async_config": {
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"enableRateLimit": true,
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"rateLimit": 200
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},
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"pair_whitelist": [
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"AGLD/USDT:USDT",
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"1INCH/USDT:USDT",
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"AAVE/USDT:USDT",
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"ALGO/USDT:USDT",
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"ALPHA/USDT:USDT",
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"API3/USDT:USDT",
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"AVAX/USDT:USDT",
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"AXS/USDT:USDT",
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"BCH/USDT:USDT"
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],
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"pair_blacklist": []
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},
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"entry_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1,
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"price_last_balance": 0.0,
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"check_depth_of_market": {
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"enabled": false,
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"bids_to_ask_delta": 1
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}
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},
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"exit_pricing": {
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"price_side": "other",
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"use_order_book": true,
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"order_book_top": 1
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},
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"pairlists": [
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{
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"method": "StaticPairList"
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}
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],
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"freqai": {
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"timeframes": [
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"3m",
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"15m",
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"1h"
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],
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"train_period": 20,
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"backtest_period": 2,
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"identifier": "example",
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"live_trained_timestamp": 0,
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"corr_pairlist": [
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"BTC/USDT:USDT",
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"ETH/USDT:USDT"
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],
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"feature_parameters": {
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"period": 20,
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"shift": 2,
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"DI_threshold": 0,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"stratify": 0
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},
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"data_split_parameters": {
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"test_size": 0.33,
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"random_state": 1
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},
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"model_training_parameters": {
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"n_estimators": 1000,
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"task_type": "CPU"
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}
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}
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}
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@ -6,7 +6,7 @@
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"fiat_display_currency": "USD",
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"dry_run": true,
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"timeframe": "5m",
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"dry_run_wallet": 1000,
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"dry_run_wallet": 4000,
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"cancel_open_orders_on_exit": true,
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"unfilledtimeout": {
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"entry": 10,
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@ -51,7 +51,8 @@
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"freqai": {
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"timeframes": [
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"5m",
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"15m"
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"15m",
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"4h"
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],
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"train_period": 30,
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"backtest_period": 7,
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@ -60,16 +61,18 @@
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"corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT",
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"DOT/USDT"
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"DOT/USDT",
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"MATIC/USDT",
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"SOL/USDT"
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],
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"feature_parameters": {
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"period": 24,
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"period": 20,
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"shift": 1,
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"DI_threshold": 0,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"stratify": 3
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"use_SVM_to_remove_outliers": false,
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"stratify": 0
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},
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"data_split_parameters": {
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"test_size": 0.33,
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@ -77,8 +80,6 @@
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},
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"model_training_parameters": {
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"n_estimators": 1000,
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"random_state": 1,
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"learning_rate": 0.1,
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"task_type": "CPU"
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}
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},
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@ -56,6 +56,7 @@ class IFreqaiModel(ABC):
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self.set_full_path()
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self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
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self.config['exchange']['pair_whitelist'])
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self.lock = threading.Lock()
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def assert_config(self, config: Dict[str, Any]) -> None:
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@ -7,8 +7,10 @@ import talib.abstract as ta
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from pandas import DataFrame
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from technical import qtpylib
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from freqtrade.exchange import timeframe_to_prev_date
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from freqtrade.freqai.strategy_bridge import CustomModel
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from freqtrade.strategy import merge_informative_pair
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from freqtrade.persistence import Trade
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from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
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from freqtrade.strategy.interface import IStrategy
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@ -46,6 +48,11 @@ class FreqaiExampleStrategy(IStrategy):
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stoploss = -0.05
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use_sell_signal = True
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startup_candle_count: int = 300
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can_short = False
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linear_roi_offset = DecimalParameter(0.00, 0.02, default=0.005, space='sell',
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optimize=False, load=True)
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max_roi_time_long = IntParameter(0, 800, default=400, space='sell', optimize=False, load=True)
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def informative_pairs(self):
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whitelist_pairs = self.dp.current_whitelist()
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@ -182,25 +189,88 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"]
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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buy_conditions = [
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(dataframe["prediction"] > dataframe["target_roi"]) & (dataframe["do_predict"] == 1)
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enter_long_conditions = [
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df['do_predict'] == 1,
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df['prediction'] > df["target_roi"]
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]
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if buy_conditions:
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dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1
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if enter_long_conditions:
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df.loc[reduce(lambda x, y: x & y,
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enter_long_conditions), ["enter_long", "enter_tag"]] = (1, 'long')
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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sell_conditions = [
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(dataframe["do_predict"] <= 0)
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enter_short_conditions = [
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df['do_predict'] == 1,
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df['prediction'] < df["sell_roi"]
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]
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if sell_conditions:
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dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1
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return dataframe
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if enter_short_conditions:
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df.loc[reduce(lambda x, y: x & y,
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enter_short_conditions), ["enter_short", "enter_tag"]] = (1, 'short')
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [
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df['do_predict'] == 1,
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df['prediction'] < df['sell_roi'] * 0.25
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]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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exit_short_conditions = [
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df['do_predict'] == 1,
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df['prediction'] > df['target_roi'] * 0.25
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]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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return df
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def get_ticker_indicator(self):
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return int(self.config["timeframe"][:-1])
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def custom_exit(self, pair: str, trade: Trade, current_time, current_rate,
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current_profit, **kwargs):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
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trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc)
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trade_candle = dataframe.loc[(dataframe['date'] == trade_date)]
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if trade_candle.empty:
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return None
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trade_candle = trade_candle.squeeze()
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pair_dict = self.model.bridge.data_drawer.pair_dict
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entry_tag = trade.enter_tag
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if 'prediction' + entry_tag not in pair_dict[pair]:
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with self.model.bridge.lock:
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self.model.bridge.data_drawer.pair_dict[pair][
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'prediction' + entry_tag] = abs(trade_candle['prediction'])
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self.model.bridge.data_drawer.save_drawer_to_disk()
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else:
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if pair_dict[pair]['prediction' + entry_tag] > 0:
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roi_price = abs(trade_candle['prediction' + entry_tag])
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else:
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with self.model.bridge.lock:
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self.model.bridge.data_drawer.pair_dict[pair][
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'prediction' + entry_tag] = abs(trade_candle['prediction'])
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self.model.bridge.data_drawer.save_drawer_to_disk()
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roi_price = abs(trade_candle['prediction'])
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roi_time = self.max_roi_time_long.value
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roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) /
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(roi_time * 60))
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if roi_decay < 0:
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roi_decay = self.linear_roi_offset.value
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else:
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roi_decay += self.linear_roi_offset.value
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if current_profit > roi_price: # roi_decay:
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with self.model.bridge.lock:
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self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0
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self.model.bridge.data_drawer.save_drawer_to_disk()
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return 'roi_custom_win'
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