Merge pull request #7950 from freqtrade/freqai_feature_engineering_functions
FreqAI Strategy - Improve user experience
This commit is contained in:
Matthias
GitHub
@@ -82,7 +82,7 @@ def test_compute_distances(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
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avg_mean_dist = freqai.dk.compute_distances()
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assert round(avg_mean_dist, 2) == 1.99
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assert round(avg_mean_dist, 2) == 1.98
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def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
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@@ -90,7 +90,7 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
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freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
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freqai.dk.use_SVM_to_remove_outliers(predict=False)
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assert log_has_re(
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"SVM detected 7.36%",
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"SVM detected 7.83%",
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caplog,
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)
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@@ -222,6 +222,9 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
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if 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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{"indicator_periods_candles": [2]})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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@@ -232,15 +235,14 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = base_df[freqai_conf["timeframe"]]
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
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for i in range(5):
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df[f'%-constant_{i}'] = i
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metadata = {"pair": "LTC/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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freqai.start_backtesting(df, metadata, freqai.dk, strategy)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == num_files
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@@ -261,6 +263,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180120-20180124"})
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freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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{"indicator_periods_candles": [2]})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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@@ -271,12 +275,11 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = base_df[freqai_conf["timeframe"]]
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metadata = {"pair": "LTC/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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freqai.start_backtesting(df, metadata, freqai.dk, strategy)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 9
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@@ -287,6 +290,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
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def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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freqai_conf.update({"timerange": "20180120-20180130"})
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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{"indicator_periods_candles": [2]})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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@@ -296,15 +301,14 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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sub_timerange = TimeRange.parse_timerange("20180101-20180130")
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_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = base_df[freqai_conf["timeframe"]]
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pair = "ADA/BTC"
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metadata = {"pair": pair}
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freqai.dk.pair = pair
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freqai.start_backtesting(df, metadata, freqai.dk)
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freqai.start_backtesting(df, metadata, freqai.dk, strategy)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 2
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@@ -322,14 +326,13 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = base_df[freqai_conf["timeframe"]]
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pair = "ADA/BTC"
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metadata = {"pair": pair}
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freqai.dk.pair = pair
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freqai.start_backtesting(df, metadata, freqai.dk)
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freqai.start_backtesting(df, metadata, freqai.dk, strategy)
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assert log_has_re(
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"Found backtesting prediction file ",
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@@ -339,7 +342,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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pair = "ETH/BTC"
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metadata = {"pair": pair}
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freqai.dk.pair = pair
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freqai.start_backtesting(df, metadata, freqai.dk)
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freqai.start_backtesting(df, metadata, freqai.dk, strategy)
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path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
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prediction_files = [x for x in path.iterdir() if x.is_file()]
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@@ -1,11 +1,10 @@
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import logging
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from functools import reduce
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, merge_informative_pair
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from freqtrade.strategy import IStrategy
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logger = logging.getLogger(__name__)
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@@ -22,52 +21,39 @@ class freqai_rl_test_strat(IStrategy):
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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startup_candle_count: int = 30
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startup_candle_count: int = 300
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can_short = False
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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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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return dataframe
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t = int(t)
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informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
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# The following columns are necessary for RL models.
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informative[f"%-{pair}raw_close"] = informative["close"]
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informative[f"%-{pair}raw_open"] = informative["open"]
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informative[f"%-{pair}raw_high"] = informative["high"]
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informative[f"%-{pair}raw_low"] = informative["low"]
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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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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return dataframe
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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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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def feature_engineering_standard(self, dataframe, **kwargs):
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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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# For RL, there are no direct targets to set. This is filler (neutral)
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# until the agent sends an action.
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df["&-action"] = 0
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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return df
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dataframe["%-raw_close"] = dataframe["close"]
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dataframe["%-raw_open"] = dataframe["open"]
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dataframe["%-raw_high"] = dataframe["high"]
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dataframe["%-raw_low"] = dataframe["low"]
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return dataframe
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def set_freqai_targets(self, dataframe, **kwargs):
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dataframe["&-action"] = 0
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@@ -2,11 +2,10 @@ import logging
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from functools import reduce
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
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logger = logging.getLogger(__name__)
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@@ -57,55 +56,35 @@ class freqai_test_classifier(IStrategy):
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informative_pairs.append((pair, tf))
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return informative_pairs
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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coin = pair.split('/')[0]
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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return dataframe
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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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def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
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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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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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return dataframe
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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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def feature_engineering_standard(self, dataframe, **kwargs):
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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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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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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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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return dataframe
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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-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down')
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def set_freqai_targets(self, dataframe, **kwargs):
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return df
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dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
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dataframe["close"], 'up', 'down')
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@@ -2,11 +2,10 @@ import logging
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from functools import reduce
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
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logger = logging.getLogger(__name__)
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@@ -44,59 +43,38 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
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)
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max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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coin = pair.split('/')[0]
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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return dataframe
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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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def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
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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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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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return dataframe
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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)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
def feature_engineering_standard(self, dataframe, **kwargs):
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
return dataframe
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
|
||||
df["close"], 'up', 'down')
|
||||
def set_freqai_targets(self, dataframe, **kwargs):
|
||||
|
||||
df['&s-up_or_down2'] = np.where(df["close"].shift(-50) >
|
||||
df["close"], 'up2', 'down2')
|
||||
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
|
||||
dataframe["close"], 'up', 'down')
|
||||
|
||||
return df
|
||||
dataframe['&s-up_or_down2'] = np.where(dataframe["close"].shift(-50) >
|
||||
dataframe["close"], 'up2', 'down2')
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
|
||||
@@ -1,11 +1,10 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -43,74 +42,53 @@ class freqai_test_multimodel_strat(IStrategy):
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
|
||||
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
|
||||
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
return dataframe
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
||||
dataframe["%-raw_volume"] = dataframe["volume"]
|
||||
dataframe["%-raw_price"] = dataframe["close"]
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
return dataframe
|
||||
|
||||
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"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
def feature_engineering_standard(self, dataframe, **kwargs):
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
return dataframe
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
def set_freqai_targets(self, dataframe, **kwargs):
|
||||
|
||||
dataframe["&-s_close"] = (
|
||||
dataframe["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ dataframe["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
df["&-s_range"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.max()
|
||||
-
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.min()
|
||||
)
|
||||
dataframe["&-s_range"] = (
|
||||
dataframe["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.max()
|
||||
-
|
||||
dataframe["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.min()
|
||||
)
|
||||
|
||||
return df
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
|
||||
@@ -1,11 +1,10 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -43,62 +42,41 @@ class freqai_test_strat(IStrategy):
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
|
||||
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
|
||||
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
return dataframe
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
||||
dataframe["%-raw_volume"] = dataframe["volume"]
|
||||
dataframe["%-raw_price"] = dataframe["close"]
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
return dataframe
|
||||
|
||||
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"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
def feature_engineering_standard(self, dataframe, **kwargs):
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
return dataframe
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
def set_freqai_targets(self, dataframe, **kwargs):
|
||||
|
||||
dataframe["&-s_close"] = (
|
||||
dataframe["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ dataframe["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return df
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
|
||||
Reference in New Issue
Block a user