mirror of
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Change config parameter names to improve clarity and consistency throughout the code (!!breaking change, please check discord support channel for migration instructions or review templates/FreqaiExampleStrategy.py config_examples/config_freqai_futures.example.json file changes!!)
This commit is contained in:
Robert Caulk
parent
819cc9c0e4
commit
607455919e
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@ -15,7 +15,7 @@
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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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"name": "binance",
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"key": "",
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"secret": "",
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"ccxt_config": {
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@ -26,15 +26,8 @@
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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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"1INCH/USDT",
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"ALGO/USDT"
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],
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"pair_blacklist": []
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},
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@ -60,29 +53,31 @@
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],
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"freqai": {
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"startup_candles": 10000,
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"timeframes": [
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"purge_old_models": true,
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"train_period_days": 15,
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"backtest_period_days": 7,
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"live_retrain_hours": 0,
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"identifier": "uniqe-id6",
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"live_trained_timestamp": 0,
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"feature_parameters": {
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"include_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": 0.001,
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"identifier": "constant_retrain_live",
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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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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/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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"label_period_candles": 20,
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"include_shifted_candles": 2,
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"DI_threshold": 0.9,
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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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"indicator_max_period": 20,
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"indicator_periods": [10, 20]
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"stratify_training_data": 0,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters": {
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"test_size": 0.33,
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@ -52,32 +52,31 @@
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],
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"freqai": {
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"startup_candles": 10000,
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"timeframes": [
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"train_period_days": 30,
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"backtest_period_days": 7,
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"live_retrain_hours": 1,
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"identifier": "example",
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"live_trained_timestamp": 0,
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"feature_parameters": {
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"include_timeframes": [
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"5m",
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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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"identifier": "example",
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"live_trained_timestamp": 0,
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"corr_pairlist": [
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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT",
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"DOT/USDT",
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"MATIC/USDT",
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"SOL/USDT"
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"ETH/USDT"
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],
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"feature_parameters": {
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"period": 500,
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"shift": 1,
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"label_period_candles": 500,
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"include_shifted_candles": 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": false,
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"stratify": 0,
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"indicator_max_period": 50,
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"indicator_periods": [10, 20]
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"stratify_training_data": 0,
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"indicator_max_period_candles": 50,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters": {
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"test_size": 0.33,
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204
docs/freqai.md
204
docs/freqai.md
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@ -77,19 +77,22 @@ config setup includes:
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```json
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"freqai": {
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"startup_candles": 10000,
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"timeframes" : ["5m","15m","4h"],
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"train_period" : 30,
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"backtest_period" : 7,
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"purge_old_models": true,
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"train_period_days" : 30,
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"backtest_period_days" : 7,
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"identifier" : "unique-id",
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"corr_pairlist": [
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"feature_parameters" : {
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"include_timeframes" : ["5m","15m","4h"],
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"include_corr_pairlist": [
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"ETH/USD",
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"LINK/USD",
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"BNB/USD"
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],
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"feature_parameters" : {
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"period": 24,
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"shift": 2,
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"label_period_candles": 24,
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"include_shifted_candles": 2,
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"weight_factor": 0,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters" : {
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"test_size": 0.25,
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@ -106,40 +109,99 @@ config setup includes:
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### Building the feature set
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!! slightly out of date, please refer to templates/FreqaiExampleStrategy.py for updated method !!
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Features are added by the user inside the `populate_any_indicators()` method of the strategy
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by prepending indicators with `%`:
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by prepending indicators with `%` and labels are added by prependng `&`. There are some important
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components/structures that the user *must* include when building their feature set. As shown below,
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`with self.model.bridge.lock:` must be used to ensure thread safety - especially when using third
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party libraries for indicator construction such as TA-lib. Another structure to consider is the
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location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
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This is where the user will add single features labels to their feature set to avoid duplication from
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various configuration paramters which multiply the feature set such as `include_timeframes`.
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```python
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def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
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informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14)
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informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative['%-' + coin + "adx"] = ta.ADX(informative, window=20)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
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informative[coin + "bb_lowerband"] = bollinger["lower"]
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informative[coin + "bb_middleband"] = bollinger["mid"]
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informative[coin + "bb_upperband"] = bollinger["upper"]
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informative['%-' + coin + "bb_width"] = (
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informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
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) / informative[coin + "bb_middleband"]
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def populate_any_indicators(
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self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
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):
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"""
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Function designed to automatically generate, name and merge features
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from user indicated timeframes in the configuration file. User controls the indicators
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passed to the training/prediction by prepending indicators with `'%-' + coin `
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(see convention below). I.e. user should not prepend any supporting metrics
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(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
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model.
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:params:
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:pair: pair to be used as informative
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:df: strategy dataframe which will receive merges from informatives
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:tf: timeframe of the dataframe which will modify the feature names
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:informative: the dataframe associated with the informative pair
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:coin: the name of the coin which will modify the feature names.
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"""
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with self.model.bridge.lock:
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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# The following code automatically adds features according to the `shift` parameter passed
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# in the config. Do not remove
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indicators = [col for col in informative if col.startswith('%')]
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for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(informative), window=t, stds=2.2
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)
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informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
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informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
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informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
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informative[f"%-{coin}bb_width-period_{t}"] = (
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informative[f"{coin}bb_upperband-period_{t}"]
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- informative[f"{coin}bb_lowerband-period_{t}"]
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) / informative[f"{coin}bb_middleband-period_{t}"]
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informative[f"%-{coin}close-bb_lower-period_{t}"] = (
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informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
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)
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informative[f"%-{coin}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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# The following code safely merges into the base timeframe.
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# Do not remove.
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]]
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# user adds targets here by prepending them with &- (see convention below)
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# If user wishes to use multiple targets, a multioutput prediction model
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# needs to be used such as templates/CatboostPredictionMultiModel.py
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df["&-s_close"] = (
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df["close"]
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.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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return df
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```
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The user of the present example does not want to pass the `bb_lowerband` as a feature to the model,
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and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
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@ -153,6 +215,7 @@ a specific pair or timeframe, they should use the following structure inside `po
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```python
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def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
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...
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# Add generalized indicators here (because in live, it will call only this function to populate
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# indicators for retraining). Notice how we ensure not to add them multiple times by associating
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@ -160,35 +223,47 @@ a specific pair or timeframe, they should use the following structure inside `po
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if pair == metadata['pair'] and tf == self.timeframe:
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df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
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df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
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# user adds targets here by prepending them with &- (see convention below)
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# If user wishes to use multiple targets, a multioutput prediction model
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# needs to be used such as templates/CatboostPredictionMultiModel.py
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df["&-s_close"] = (
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df["close"]
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.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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)
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```
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(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
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The `timeframes` from the example config above are the timeframes of each `populate_any_indicator()`
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The `include_timeframes` from the example config above are the timeframes of each `populate_any_indicator()`
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included metric for inclusion in the feature set. In the present case, the user is asking for the
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`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included
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in the feature set.
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In addition, the user can ask for each of these features to be included from
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informative pairs using the `corr_pairlist`. This means that the present feature
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set will include all the `base_features` on all the `timeframes` for each of
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informative pairs using the `include_corr_pairlist`. This means that the present feature
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set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of
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`ETH/USD`, `LINK/USD`, and `BNB/USD`.
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`shift` is another user controlled parameter which indicates the number of previous
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candles to include in the present feature set. In other words, `shift: 2`, tells
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`include_shifted_candles` is another user controlled parameter which indicates the number of previous
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candles to include in the present feature set. In other words, `innclude_shifted_candles: 2`, tells
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Freqai to include the the past 2 candles for each of the features included
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in the dataset.
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In total, the number of features the present user has created is:_
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no. `timeframes` * no. `base_features` * no. `corr_pairlist` * no. `shift`_
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3 * 3 * 3 * 2 = 54._
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legnth of `include_timeframes` * no. features in `populate_any_indicators()` * legnth of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`_
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3 * 3 * 3 * 2 * 2 = 108._
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### Deciding the sliding training window and backtesting duration
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Users define the backtesting timerange with the typical `--timerange` parameter in the user
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configuration file. `train_period` is the duration of the sliding training window, while
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`backtest_period` is the sliding backtesting window, both in number of days (backtest_period can be
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configuration file. `train_period_days` is the duration of the sliding training window, while
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`backtest_period_days` is the sliding backtesting window, both in number of days (backtest_period_days can be
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a float to indicate sub daily retraining in live/dry mode). In the present example,
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the user is asking Freqai to use a training period of 30 days and backtest the subsequent 7 days.
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This means that if the user sets `--timerange 20210501-20210701`,
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@ -203,9 +278,9 @@ the user must manually enter the required number of `startup_candles` in the con
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is used to increase the available data to FreqAI and should be sufficient to enable all indicators
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to be NaN free at the beginning of the first training timerange. This boils down to identifying the
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highest timeframe (`4h` in present example) and the longest indicator period (25 in present example)
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and adding this to the `train_period`. The units need to be in the base candle time frame:_
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and adding this to the `train_period_days`. The units need to be in the base candle time frame:_
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`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period * 1440 minutes per day ) / 5 min (base time frame) = 1488.
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`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488.
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!!! Note
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In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live.
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@ -242,9 +317,9 @@ The Freqai strategy requires the user to include the following lines of code in
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def informative_pairs(self):
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whitelist_pairs = self.dp.current_whitelist()
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corr_pairs = self.config["freqai"]["corr_pairlist"]
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corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
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informative_pairs = []
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for tf in self.config["freqai"]["timeframes"]:
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for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
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for pair in whitelist_pairs:
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informative_pairs.append((pair, tf))
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for pair in corr_pairs:
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@ -257,19 +332,35 @@ The Freqai strategy requires the user to include the following lines of code in
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self.model = CustomModel(self.config)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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self.freqai_info = self.config['freqai']
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self.freqai_info = self.config["freqai"]
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self.pair = metadata["pair"]
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sgi = True
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# the following loops are necessary for building the features
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# indicated by the user in the configuration file.
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for tf in self.freqai_info['timeframes']:
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for i in self.freqai_info['corr_pairlist']:
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dataframe = self.populate_any_indicators(i,
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dataframe.copy(), tf, coin=i.split("/")[0]+'-')
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# All indicators must be populated by populate_any_indicators() for live functionality
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# to work correctly.
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for tf in self.freqai_info["feature_parameters"]["include_timeframes"]:
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dataframe = self.populate_any_indicators(
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metadata,
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self.pair,
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dataframe.copy(),
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tf,
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coin=self.pair.split("/")[0] + "-",
|
||||
set_generalized_indicators=sgi,
|
||||
)
|
||||
sgi = False
|
||||
for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]:
|
||||
if metadata["pair"] in pair:
|
||||
continue # do not include whitelisted pair twice if it is in corr_pairlist
|
||||
dataframe = self.populate_any_indicators(
|
||||
metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
|
||||
)
|
||||
|
||||
# the model will return 4 values, its prediction, an indication of whether or not the prediction
|
||||
# should be accepted, the target mean/std values from the labels used during each training period.
|
||||
(dataframe['prediction'], dataframe['do_predict'],
|
||||
dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)
|
||||
# the model will return 4 values, its prediction, an indication of whether or not the
|
||||
# prediction should be accepted, the target mean/std values from the labels used during
|
||||
# each training period.
|
||||
dataframe = self.model.bridge.start(dataframe, metadata, self)
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
|
@ -280,8 +371,7 @@ the feature set with a proper naming convention for the IFreqaiModel to use late
|
|||
### Building an IFreqaiModel
|
||||
|
||||
Freqai has an example prediction model based on the popular `Catboost` regression (`freqai/prediction_models/CatboostPredictionModel.py`). However, users can customize and create
|
||||
their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()`, `predict()`,
|
||||
and `make_labels()` to let them customize various aspects of their training procedures.
|
||||
their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
|
||||
|
||||
### Running the model live
|
||||
|
||||
|
|
@ -293,10 +383,10 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.
|
|||
|
||||
By default, Freqai will not find find any existing models and will start by training a new one
|
||||
given the user configuration settings. Following training, it will use that model to predict for the
|
||||
duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain
|
||||
duration of `backtest_period_days`. After a full `backtest_period_days` has elapsed, Freqai will auto retrain
|
||||
a new model, and begin making predictions with the updated model. FreqAI backtesting and live both
|
||||
permit the user to use fractional days (i.e. 0.1) in the `backtest_period`, which enables more frequent
|
||||
retraining. But the user should be careful that using a fractional `backtest_period` with a large
|
||||
permit the user to use fractional days (i.e. 0.1) in the `backtest_period_days`, which enables more frequent
|
||||
retraining. But the user should be careful that using a fractional `backtest_period_days` with a large
|
||||
`--timerange` in backtesting will result in a huge amount of required trainings/models.
|
||||
|
||||
If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
|
||||
|
|
@ -305,12 +395,14 @@ the same `identifier` parameter
|
|||
```json
|
||||
"freqai": {
|
||||
"identifier": "example",
|
||||
"live_retrain_hours": 1
|
||||
}
|
||||
```
|
||||
|
||||
In this case, although Freqai will initiate with a
|
||||
pre-trained model, it will still check to see how much time has elapsed since the model was trained,
|
||||
and if a full `backtest_period` has elapsed since the end of the loaded model, FreqAI will self retrain.
|
||||
and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain.
|
||||
It is common to want constant retraining, in whichcase, user should set `live_retrain_hours` to 0.
|
||||
|
||||
## Data anylsis techniques
|
||||
|
||||
|
|
@ -412,7 +504,7 @@ The user can stratify the training/testing data using:
|
|||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"stratify": 3
|
||||
"stratify_training_data": 3
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
|
|||
|
|
@ -174,6 +174,7 @@ def _validate_freqai(conf: Dict[str, Any]) -> None:
|
|||
|
||||
for param in constants.SCHEMA_FREQAI_REQUIRED:
|
||||
if param not in conf.get('freqai', {}):
|
||||
if param not in conf.get('freqai', {}).get('feature_parameters', {}):
|
||||
raise OperationalException(
|
||||
f'{param} not found in Freqai config'
|
||||
)
|
||||
|
|
|
|||
|
|
@ -477,16 +477,16 @@ CONF_SCHEMA = {
|
|||
"freqai": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"timeframes": {"type": "list"},
|
||||
"train_period": {"type": "integer", "default": 0},
|
||||
"backtest_period": {"type": "float", "default": 7},
|
||||
"train_period_days": {"type": "integer", "default": 0},
|
||||
"backtest_period_days": {"type": "float", "default": 7},
|
||||
"identifier": {"type": "str", "default": "example"},
|
||||
"corr_pairlist": {"type": "list"},
|
||||
"feature_parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"period": {"type": "integer"},
|
||||
"shift": {"type": "integer", "default": 0},
|
||||
"include_corr_pairlist": {"type": "list"},
|
||||
"include_timeframes": {"type": "list"},
|
||||
"label_period_candles": {"type": "integer"},
|
||||
"include_shifted_candles": {"type": "integer", "default": 0},
|
||||
"DI_threshold": {"type": "float", "default": 0},
|
||||
"weight_factor": {"type": "number", "default": 0},
|
||||
"principal_component_analysis": {"type": "boolean", "default": False},
|
||||
|
|
@ -555,11 +555,11 @@ SCHEMA_MINIMAL_REQUIRED = [
|
|||
]
|
||||
|
||||
SCHEMA_FREQAI_REQUIRED = [
|
||||
'timeframes',
|
||||
'train_period',
|
||||
'backtest_period',
|
||||
'include_timeframes',
|
||||
'train_period_days',
|
||||
'backtest_period_days',
|
||||
'identifier',
|
||||
'corr_pairlist',
|
||||
'include_corr_pairlist',
|
||||
'feature_parameters',
|
||||
'data_split_parameters',
|
||||
'model_training_parameters'
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ from freqtrade.strategy.interface import IStrategy
|
|||
|
||||
|
||||
SECONDS_IN_DAY = 86400
|
||||
SECONDS_IN_HOUR = 3600
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -59,13 +60,13 @@ class FreqaiDataKitchen:
|
|||
self.set_all_pairs()
|
||||
if not self.live:
|
||||
self.full_timerange = self.create_fulltimerange(
|
||||
self.config["timerange"], self.freqai_config.get("train_period")
|
||||
self.config["timerange"], self.freqai_config.get("train_period_days")
|
||||
)
|
||||
|
||||
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
|
||||
self.full_timerange,
|
||||
config["freqai"]["train_period"],
|
||||
config["freqai"]["backtest_period"],
|
||||
config["freqai"]["train_period_days"],
|
||||
config["freqai"]["backtest_period_days"],
|
||||
)
|
||||
# self.strat_dataframe: DataFrame = strat_dataframe
|
||||
self.dd = data_drawer
|
||||
|
|
@ -234,17 +235,18 @@ class FreqaiDataKitchen:
|
|||
:filtered_dataframe: cleaned dataframe ready to be split.
|
||||
:labels: cleaned labels ready to be split.
|
||||
"""
|
||||
feat_dict = self.freqai_config.get("feature_parameters", {})
|
||||
|
||||
weights: npt.ArrayLike
|
||||
if self.freqai_config["feature_parameters"].get("weight_factor", 0) > 0:
|
||||
if feat_dict.get("weight_factor", 0) > 0:
|
||||
weights = self.set_weights_higher_recent(len(filtered_dataframe))
|
||||
else:
|
||||
weights = np.ones(len(filtered_dataframe))
|
||||
|
||||
if self.freqai_config["feature_parameters"].get("stratify", 0) > 0:
|
||||
if feat_dict.get("stratify_training_data", 0) > 0:
|
||||
stratification = np.zeros(len(filtered_dataframe))
|
||||
for i in range(1, len(stratification)):
|
||||
if i % self.freqai_config.get("feature_parameters", {}).get("stratify", 0) == 0:
|
||||
if i % feat_dict.get("stratify_training_data", 0) == 0:
|
||||
stratification[i] = 1
|
||||
else:
|
||||
stratification = None
|
||||
|
|
@ -439,7 +441,7 @@ class FreqaiDataKitchen:
|
|||
bt_split: the backtesting length (dats). Specified in user configuration file
|
||||
"""
|
||||
|
||||
train_period = train_split * SECONDS_IN_DAY
|
||||
train_period_days = train_split * SECONDS_IN_DAY
|
||||
bt_period = bt_split * SECONDS_IN_DAY
|
||||
|
||||
full_timerange = TimeRange.parse_timerange(tr)
|
||||
|
|
@ -460,7 +462,7 @@ class FreqaiDataKitchen:
|
|||
while True:
|
||||
if not first:
|
||||
timerange_train.startts = timerange_train.startts + bt_period
|
||||
timerange_train.stopts = timerange_train.startts + train_period
|
||||
timerange_train.stopts = timerange_train.startts + train_period_days
|
||||
|
||||
first = False
|
||||
start = datetime.datetime.utcfromtimestamp(timerange_train.startts)
|
||||
|
|
@ -763,7 +765,7 @@ class FreqaiDataKitchen:
|
|||
|
||||
return
|
||||
|
||||
def create_fulltimerange(self, backtest_tr: str, backtest_period: int) -> str:
|
||||
def create_fulltimerange(self, backtest_tr: str, backtest_period_days: int) -> str:
|
||||
backtest_timerange = TimeRange.parse_timerange(backtest_tr)
|
||||
|
||||
if backtest_timerange.stopts == 0:
|
||||
|
|
@ -771,7 +773,8 @@ class FreqaiDataKitchen:
|
|||
datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
|
||||
)
|
||||
|
||||
backtest_timerange.startts = backtest_timerange.startts - backtest_period * SECONDS_IN_DAY
|
||||
backtest_timerange.startts = (backtest_timerange.startts
|
||||
- backtest_period_days * SECONDS_IN_DAY)
|
||||
start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts)
|
||||
stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts)
|
||||
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
|
||||
|
|
@ -817,7 +820,8 @@ class FreqaiDataKitchen:
|
|||
data_load_timerange = TimeRange()
|
||||
|
||||
# find the max indicator length required
|
||||
max_timeframe_chars = self.freqai_config.get("timeframes")[-1]
|
||||
max_timeframe_chars = self.freqai_config.get(
|
||||
"feature_parameters", {}).get("include_timeframes")[-1]
|
||||
max_period = self.freqai_config.get("feature_parameters", {}).get(
|
||||
"indicator_max_period", 50
|
||||
)
|
||||
|
|
@ -840,11 +844,11 @@ class FreqaiDataKitchen:
|
|||
# logger.info(f'Extending data download by {additional_seconds/SECONDS_IN_DAY:.2f} days')
|
||||
|
||||
if trained_timestamp != 0:
|
||||
elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY
|
||||
retrain = elapsed_time > self.freqai_config.get("backtest_period")
|
||||
elapsed_time = (time - trained_timestamp) / SECONDS_IN_HOUR
|
||||
retrain = elapsed_time > self.freqai_config.get("live_retrain_hours", 0)
|
||||
if retrain:
|
||||
trained_timerange.startts = int(
|
||||
time - self.freqai_config.get("train_period", 0) * SECONDS_IN_DAY
|
||||
time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
||||
)
|
||||
trained_timerange.stopts = int(time)
|
||||
# we want to load/populate indicators on more data than we plan to train on so
|
||||
|
|
@ -852,19 +856,19 @@ class FreqaiDataKitchen:
|
|||
# unless they have data further back in time before the start of the train period
|
||||
data_load_timerange.startts = int(
|
||||
time
|
||||
- self.freqai_config.get("train_period", 0) * SECONDS_IN_DAY
|
||||
- self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
||||
- additional_seconds
|
||||
)
|
||||
data_load_timerange.stopts = int(time)
|
||||
else: # user passed no live_trained_timerange in config
|
||||
trained_timerange.startts = int(
|
||||
time - self.freqai_config.get("train_period") * SECONDS_IN_DAY
|
||||
time - self.freqai_config.get("train_period_days") * SECONDS_IN_DAY
|
||||
)
|
||||
trained_timerange.stopts = int(time)
|
||||
|
||||
data_load_timerange.startts = int(
|
||||
time
|
||||
- self.freqai_config.get("train_period", 0) * SECONDS_IN_DAY
|
||||
- self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
||||
- additional_seconds
|
||||
)
|
||||
data_load_timerange.stopts = int(time)
|
||||
|
|
@ -930,7 +934,7 @@ class FreqaiDataKitchen:
|
|||
refresh_backtest_ohlcv_data(
|
||||
exchange,
|
||||
pairs=self.all_pairs,
|
||||
timeframes=self.freqai_config.get("timeframes"),
|
||||
timeframes=self.freqai_config.get("feature_parameters", {}).get("include_timeframes"),
|
||||
datadir=self.config["datadir"],
|
||||
timerange=timerange,
|
||||
new_pairs_days=new_pairs_days,
|
||||
|
|
@ -948,12 +952,12 @@ class FreqaiDataKitchen:
|
|||
:params:
|
||||
dataframe: DataFrame = strategy provided dataframe
|
||||
"""
|
||||
|
||||
feat_params = self.freqai_config.get("feature_parameters", {})
|
||||
with self.dd.history_lock:
|
||||
history_data = self.dd.historic_data
|
||||
|
||||
for pair in self.all_pairs:
|
||||
for tf in self.freqai_config.get("timeframes"):
|
||||
for tf in feat_params.get("include_timeframes"):
|
||||
|
||||
# check if newest candle is already appended
|
||||
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
|
||||
|
|
@ -992,7 +996,8 @@ class FreqaiDataKitchen:
|
|||
|
||||
def set_all_pairs(self) -> None:
|
||||
|
||||
self.all_pairs = copy.deepcopy(self.freqai_config.get("corr_pairlist", []))
|
||||
self.all_pairs = copy.deepcopy(self.freqai_config.get(
|
||||
'feature_parameters', {}).get('include_corr_pairlist', []))
|
||||
for pair in self.config.get("exchange", "").get("pair_whitelist"):
|
||||
if pair not in self.all_pairs:
|
||||
self.all_pairs.append(pair)
|
||||
|
|
@ -1003,14 +1008,14 @@ class FreqaiDataKitchen:
|
|||
Only called once upon startup of bot.
|
||||
:params:
|
||||
timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period
|
||||
for training according to user defined train_period_days
|
||||
"""
|
||||
history_data = self.dd.historic_data
|
||||
|
||||
for pair in self.all_pairs:
|
||||
if pair not in history_data:
|
||||
history_data[pair] = {}
|
||||
for tf in self.freqai_config.get("timeframes"):
|
||||
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
|
||||
history_data[pair][tf] = load_pair_history(
|
||||
datadir=self.config["datadir"],
|
||||
timeframe=tf,
|
||||
|
|
@ -1028,7 +1033,7 @@ class FreqaiDataKitchen:
|
|||
to the present pair.
|
||||
:params:
|
||||
timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period
|
||||
for training according to user defined train_period_days
|
||||
metadata: dict = strategy furnished pair metadata
|
||||
"""
|
||||
|
||||
|
|
@ -1036,9 +1041,10 @@ class FreqaiDataKitchen:
|
|||
corr_dataframes: Dict[Any, Any] = {}
|
||||
base_dataframes: Dict[Any, Any] = {}
|
||||
historic_data = self.dd.historic_data
|
||||
pairs = self.freqai_config.get("corr_pairlist", [])
|
||||
pairs = self.freqai_config.get('feature_parameters', {}).get(
|
||||
'include_corr_pairlist', [])
|
||||
|
||||
for tf in self.freqai_config.get("timeframes"):
|
||||
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
|
||||
base_dataframes[tf] = self.slice_dataframe(timerange, historic_data[pair][tf])
|
||||
if pairs:
|
||||
for p in pairs:
|
||||
|
|
@ -1057,7 +1063,7 @@ class FreqaiDataKitchen:
|
|||
# DataFrame]:
|
||||
# corr_dataframes: Dict[Any, Any] = {}
|
||||
# base_dataframes: Dict[Any, Any] = {}
|
||||
# pairs = self.freqai_config.get('corr_pairlist', []) # + [metadata['pair']]
|
||||
# pairs = self.freqai_config.get('include_corr_pairlist', []) # + [metadata['pair']]
|
||||
# # timerange = TimeRange.parse_timerange(new_timerange)
|
||||
|
||||
# for tf in self.freqai_config.get('timeframes'):
|
||||
|
|
@ -1101,9 +1107,9 @@ class FreqaiDataKitchen:
|
|||
dataframe: DataFrame = dataframe containing populated indicators
|
||||
"""
|
||||
dataframe = base_dataframes[self.config["timeframe"]].copy()
|
||||
pairs = self.freqai_config.get("corr_pairlist", [])
|
||||
pairs = self.freqai_config.get('feature_parameters', {}).get('include_corr_pairlist', [])
|
||||
sgi = True
|
||||
for tf in self.freqai_config.get("timeframes"):
|
||||
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
|
||||
dataframe = strategy.populate_any_indicators(
|
||||
pair,
|
||||
pair,
|
||||
|
|
|
|||
|
|
@ -95,7 +95,7 @@ class IFreqaiModel(ABC):
|
|||
dk = self.start_live(dataframe, metadata, strategy, self.dk)
|
||||
|
||||
# For backtesting, each pair enters and then gets trained for each window along the
|
||||
# sliding window defined by "train_period" (training window) and "backtest_period"
|
||||
# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
|
||||
# (backtest window, i.e. window immediately following the training window).
|
||||
# FreqAI slides the window and sequentially builds the backtesting results before returning
|
||||
# the concatenated results for the full backtesting period back to the strategy.
|
||||
|
|
@ -143,11 +143,11 @@ class IFreqaiModel(ABC):
|
|||
) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for backtesting. For backtesting, each pair enters and then gets
|
||||
trained for each window along the sliding window defined by "train_period" (training window)
|
||||
and "backtest_period" (backtest window, i.e. window immediately following the
|
||||
training window). FreqAI slides the window and sequentially builds the backtesting results
|
||||
before returning the concatenated results for the full backtesting period back to the
|
||||
strategy.
|
||||
trained for each window along the sliding window defined by "train_period_days"
|
||||
(training window) and "backtest_period_days" (backtest window, i.e. window immediately
|
||||
following the training window). FreqAI slides the window and sequentially builds
|
||||
the backtesting results before returning the concatenated results for the full
|
||||
backtesting period back to the strategy.
|
||||
:params:
|
||||
dataframe: DataFrame = strategy passed dataframe
|
||||
metadata: Dict = pair metadata
|
||||
|
|
|
|||
|
|
@ -27,29 +27,11 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
|
||||
return dataframe
|
||||
|
||||
def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
"""
|
||||
User defines the labels here (target values).
|
||||
:params:
|
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:dataframe: the full dataframe for the present training period
|
||||
"""
|
||||
|
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dataframe["s"] = (
|
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dataframe["close"]
|
||||
.shift(-self.feature_parameters["period"])
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.rolling(self.feature_parameters["period"])
|
||||
.mean()
|
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/ dataframe["close"]
|
||||
- 1
|
||||
)
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||||
|
||||
return dataframe["s"]
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:params:
|
||||
:unfiltered_dataframe: Full dataframe for the current training period
|
||||
|
|
@ -60,7 +42,6 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
|
||||
logger.info("--------------------Starting training " f"{pair} --------------------")
|
||||
|
||||
# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
|
|
|
|||
|
|
@ -44,7 +44,8 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
|
|||
|
||||
def dynamic_expand_pairlist(config: dict, markets: list) -> List[str]:
|
||||
if config.get('freqai', {}):
|
||||
full_pairs = config['pairs'] + [pair for pair in config['freqai']['corr_pairlist']
|
||||
corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist']
|
||||
full_pairs = config['pairs'] + [pair for pair in corr_pairlist
|
||||
if pair not in config['pairs']]
|
||||
expanded_pairs = expand_pairlist(full_pairs, markets)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -56,9 +56,9 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["corr_pairlist"]
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["timeframes"]:
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
|
|
@ -93,7 +93,7 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods"]:
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
|
|
@ -123,8 +123,6 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
)
|
||||
|
||||
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
||||
macd = ta.MACD(informative, timeperiod=t)
|
||||
informative[f"%-{coin}macd-period_{t}"] = macd["macd"]
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
|
|
@ -136,7 +134,7 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
|
|
@ -161,8 +159,8 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["period"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["period"])
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
|
|
@ -179,7 +177,7 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
# indicated by the user in the configuration file.
|
||||
# All indicators must be populated by populate_any_indicators() for live functionality
|
||||
# to work correctly.
|
||||
for tf in self.freqai_info["timeframes"]:
|
||||
for tf in self.freqai_info["feature_parameters"]["include_timeframes"]:
|
||||
dataframe = self.populate_any_indicators(
|
||||
metadata,
|
||||
self.pair,
|
||||
|
|
@ -189,7 +187,7 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
set_generalized_indicators=sgi,
|
||||
)
|
||||
sgi = False
|
||||
for pair in self.freqai_info["corr_pairlist"]:
|
||||
for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]:
|
||||
if metadata["pair"] in pair:
|
||||
continue # do not include whitelisted pair twice if it is in corr_pairlist
|
||||
dataframe = self.populate_any_indicators(
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user