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Update more documentation
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Matthias
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@ -82,7 +82,8 @@ positional arguments:
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new-hyperopt Create new hyperopt
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new-strategy Create new strategy
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download-data Download backtesting data.
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convert-data Convert candle (OHLCV) data from one format to another.
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convert-data Convert candle (OHLCV) data from one format to
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another.
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convert-trade-data Convert trade data from one format to another.
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backtesting Backtesting module.
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edge Edge module.
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@ -94,7 +95,7 @@ positional arguments:
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list-markets Print markets on exchange.
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list-pairs Print pairs on exchange.
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list-strategies Print available strategies.
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list-timeframes Print available ticker intervals (timeframes) for the exchange.
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list-timeframes Print available timeframes for the exchange.
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show-trades Show trades.
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test-pairlist Test your pairlist configuration.
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plot-dataframe Plot candles with indicators.
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@ -12,7 +12,7 @@ real data. This is what we call
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[backtesting](https://en.wikipedia.org/wiki/Backtesting).
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Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
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If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
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If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
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For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
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The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
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@ -35,7 +35,7 @@ freqtrade backtesting
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#### With 1 min candle (OHLCV) data
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```bash
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freqtrade backtesting --ticker-interval 1m
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freqtrade backtesting --timeframe 1m
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```
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#### Using a different on-disk historical candle (OHLCV) data source
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@ -58,7 +58,7 @@ Where `-s SampleStrategy` refers to the class name within the strategy file `sam
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#### Comparing multiple Strategies
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```bash
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freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --ticker-interval 5m
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freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
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```
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Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
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@ -228,13 +228,13 @@ You can then load the trades to perform further analysis as shown in our [data a
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To compare multiple strategies, a list of Strategies can be provided to backtesting.
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This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
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This is limited to 1 timeframe value per run. However, data is only loaded once from disk so if you have multiple
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strategies you'd like to compare, this will give a nice runtime boost.
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All listed Strategies need to be in the same directory.
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``` bash
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freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades
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freqtrade backtesting --timerange 20180401-20180410 --timeframe 5m --strategy-list Strategy001 Strategy002 --export trades
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```
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This will save the results to `user_data/backtest_results/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
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@ -9,22 +9,35 @@ This page explains the different parameters of the bot and how to run it.
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```
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usage: freqtrade [-h] [-V]
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{trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
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{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
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...
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Free, open source crypto trading bot
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positional arguments:
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{trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
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{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
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trade Trade module.
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create-userdir Create user-data directory.
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new-config Create new config
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new-hyperopt Create new hyperopt
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new-strategy Create new strategy
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download-data Download backtesting data.
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convert-data Convert candle (OHLCV) data from one format to
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another.
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convert-trade-data Convert trade data from one format to another.
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backtesting Backtesting module.
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edge Edge module.
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hyperopt Hyperopt module.
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create-userdir Create user-data directory.
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hyperopt-list List Hyperopt results
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hyperopt-show Show details of Hyperopt results
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list-exchanges Print available exchanges.
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list-timeframes Print available ticker intervals (timeframes) for the
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exchange.
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download-data Download backtesting data.
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list-hyperopts Print available hyperopt classes.
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list-markets Print markets on exchange.
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list-pairs Print pairs on exchange.
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list-strategies Print available strategies.
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list-timeframes Print available timeframes for the exchange.
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show-trades Show trades.
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test-pairlist Test your pairlist configuration.
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plot-dataframe Plot candles with indicators.
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plot-profit Generate plot showing profits.
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@ -156,7 +156,7 @@ Edge module has following configuration options:
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| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
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| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
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| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
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| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe (ticker interval). As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
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| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
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| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
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## Running Edge independently
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@ -412,7 +412,7 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
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| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
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| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
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These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
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These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
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If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
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@ -142,7 +142,7 @@ By letting the bot know how much history is needed, backtest trades can start at
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Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
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``` bash
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freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
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freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
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```
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Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
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@ -62,7 +62,7 @@ $ freqtrade new-config --config config_binance.json
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? Please insert your stake currency: BTC
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? Please insert your stake amount: 0.05
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? Please insert max_open_trades (Integer or 'unlimited'): 3
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? Please insert your timeframe (ticker interval): 5m
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? Please insert your desired timeframe (e.g. 5m): 5m
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? Please insert your display Currency (for reporting): USD
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? Select exchange binance
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? Do you want to enable Telegram? No
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@ -318,7 +318,7 @@ class Arguments:
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# Add list-timeframes subcommand
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list_timeframes_cmd = subparsers.add_parser(
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'list-timeframes',
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help='Print available ticker intervals (timeframes) for the exchange.',
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help='Print available timeframes for the exchange.',
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parents=[_common_parser],
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)
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list_timeframes_cmd.set_defaults(func=start_list_timeframes)
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