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adapt to the new strategy

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This commit is contained in:
longyu 2023-01-31 19:44:32 +01:00
parent d595d9000d
commit f61bd549b2
4 changed files with 185 additions and 103 deletions
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1
freqtrade/freqai/prediction_models/CatboostRegressor.py
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@ -49,6 +49,7 @@ class CatboostRegressor(BaseRegressionModel):
)
model.fit(X=train_data, eval_set=test_data, init_model=init_model,
verbose_eval=100,
log_cout=sys.stdout, log_cerr=sys.stderr)
return model

1
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
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@ -62,6 +62,7 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
fit_params.append({
'eval_set': eval_sets[i], 'init_model': init_models[i],
'log_cout': sys.stdout, 'log_cerr': sys.stderr,
'verbose_eval': 100,
})
model = FreqaiMultiOutputRegressor(estimator=cbr)

2
freqtrade/resolvers/strategy_resolver.py
View File

@ -261,7 +261,7 @@ class StrategyResolver(IResolver):
)
if strategy:
strategy.can_short = config["trading_mode"] == "futures" and strategy.can_short
return StrategyResolver.validate_strategy(strategy)
raise OperationalException(

284
user_data/strategies/FreqaiBinaryClassStrategy_v4.py
View File

@ -198,6 +198,16 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
"color": "#ffffff",
"type": "line"
}
},
"di+-": {
"di-": {
"color": "#e5e95b",
"type": "line"
},
"di+": {
"color": "#cda122",
"type": "line"
}
}
}
}
@ -209,6 +219,7 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
use_exit_signal = True
startup_candle_count: int = 300
can_short = True
panic_market = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
@ -231,123 +242,184 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
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_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
out = adx(informative["high"], informative["low"], informative["close"], window=t)
informative[f"%-{pair}adx-period_{t}"] = out["ADX_14"]
informative[f"%-{pair}diplus-period_{t}"] = out["DMP_14"]
informative[f"%-{pair}diminus-period_{t}"] = out["DMN_14"]
informative[f"{pair}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
#informative[f"{pair}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}close_over_20sma-period_{t}"] = (
informative["close"] / informative[f"{pair}20sma-period_{t}"]
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
out = adx(dataframe["high"], dataframe["low"], dataframe["close"], window=period)
dataframe["%-adx-period"] = out["ADX_14"]
dataframe["%-diplus-period"] = out["DMP_14"]
dataframe["%-diminus-period"] = out["DMN_14"]
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
dataframe[f"%-close_over_20sma-period"] = (
dataframe["close"] / dataframe["%-sma-period"]
)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
macd = ta.MACD(informative, timeperiod=t)
informative[f"%-{pair}macd-period_{t}"] = macd["macd"]
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
macd = ta.MACD(dataframe, timeperiod=period)
dataframe["-macd-period"] = macd["macd"]
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
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)
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe: DataFrame, **kwargs):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
minmax = np.array(["neutral"] * len(dataframe))
min_labels, max_labels = find_labels(dataframe, alpha=-0.5)
minmax[min_labels == 1] = "min"
minmax[max_labels == 1] = "max"
dataframe["&s-minmax"] = np.array([str(x) for x in minmax]).astype(np.object0)
return dataframe
# find support levels
if tf == self.freqai_info["feature_parameters"]["include_timeframes"][-1]:
informative_6h = resample_to_interval(informative, "6h")
informative_6h["support_levels"] = find_support_levels(informative_6h)
df = merge_informative_pair(df, informative_6h, self.config["timeframe"], "6h", ffill=True)
# def populate_any_indicators(
# self, pair, df, tf, informative=None, set_generalized_indicators=False
# ):
# """
# Function designed to automatically generate, name and merge features
# from user indicated timeframes in the configuration file. User controls the indicators
# passed to the training/prediction by prepending indicators with `'%-' + coin `
# (see convention below). I.e. user should not prepend any supporting metrics
# (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
# model.
# :params:
# :pair: pair to be used as informative
# :df: strategy dataframe which will receive merges from informatives
# :tf: timeframe of the dataframe which will modify the feature names
# :informative: the dataframe associated with the informative pair
# :coin: the name of the coin which will modify the feature names.
# """
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
# coin = pair.split('/')[0]
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# if informative is None:
# informative = self.dp.get_pair_dataframe(pair, tf)
# 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
# # first loop is automatically duplicating indicators for time periods
# for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
# 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-minima"] = FreqaiBinaryClassStrategy.get_min_labels(df)
#df["&s-maxima"] = FreqaiBinaryClassStrategy.get_max_labels(df)
minmax = np.array(["neutral"] * len(df))
min_labels, max_labels = find_labels(df, alpha=-0.5)
minmax[min_labels == 1] = "min"
minmax[max_labels == 1] = "max"
df["&s-minmax"] = np.array([str(x) for x in minmax]).astype(np.object0)
return df
# t = int(t)
# informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
# informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
# out = adx(informative["high"], informative["low"], informative["close"], window=t)
# informative[f"%-{pair}adx-period_{t}"] = out["ADX_14"]
# informative[f"%-{pair}diplus-period_{t}"] = out["DMP_14"]
# informative[f"%-{pair}diminus-period_{t}"] = out["DMN_14"]
# informative[f"{pair}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
# #informative[f"{pair}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
# informative[f"%-{pair}close_over_20sma-period_{t}"] = (
# informative["close"] / informative[f"{pair}20sma-period_{t}"]
# )
# bollinger = qtpylib.bollinger_bands(
# qtpylib.typical_price(informative), window=t, stds=2.2
# )
# informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
# informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
# informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
# informative[f"%-{pair}bb_width-period_{t}"] = (
# informative[f"{pair}bb_upperband-period_{t}"]
# - informative[f"{pair}bb_lowerband-period_{t}"]
# ) / informative[f"{pair}bb_middleband-period_{t}"]
# informative[f"%-{pair}close-bb_lower-period_{t}"] = (
# informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
# )
# informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
# macd = ta.MACD(informative, timeperiod=t)
# informative[f"%-{pair}macd-period_{t}"] = macd["macd"]
# informative[f"%-{pair}relative_volume-period_{t}"] = (
# informative["volume"] / informative["volume"].rolling(t).mean()
# )
# informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
# informative[f"%-{pair}raw_volume"] = informative["volume"]
# informative[f"%-{pair}raw_price"] = informative["close"]
# 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)
# # find support levels
# if tf == self.freqai_info["feature_parameters"]["include_timeframes"][-1]:
# informative_6h = resample_to_interval(informative, "6h")
# informative_6h["support_levels"] = find_support_levels(informative_6h)
# df = merge_informative_pair(df, informative_6h, self.config["timeframe"], "6h", ffill=True)
# 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)
# # 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
# # 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-minima"] = FreqaiBinaryClassStrategy.get_min_labels(df)
# #df["&s-maxima"] = FreqaiBinaryClassStrategy.get_max_labels(df)
# minmax = np.array(["neutral"] * len(df))
# min_labels, max_labels = find_labels(df, alpha=-0.5)
# minmax[min_labels == 1] = "min"
# minmax[max_labels == 1] = "max"
# df["&s-minmax"] = np.array([str(x) for x in minmax]).astype(np.object0)
# return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
# All indicators must be populated by feature_engineering_*() functions
# the model will return all labels created by user in `feature_engineering_*`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `set_freqai_targets()` for each training period.
# 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.freqai.start(dataframe, metadata, self)
# dataframe["&s-minima"] = dataframe["&s-minima"].astype(np.float32)
# dataframe["&s-maxima"] = dataframe["&s-maxima"].astype(np.float32)
min_labels, max_labels = find_labels(dataframe, alpha=-0.5)
@ -362,8 +434,15 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
hours_candle_stability = 4
if df["do_predict"].rolling(12 * hours_candle_stability).sum().iloc[-1] == 12 * hours_candle_stability: # enter the market if last `hours_candle_stability` are stable
last_candles_are_stable = True
if self.panic_market:
hours_candle_stability = 4
# enter the market if last `hours_candle_stability` are stable
last_candles_are_stable = df["do_predict"].rolling(12 * hours_candle_stability).sum().iloc[-1] == 12 * hours_candle_stability
if last_candles_are_stable:
self.panic_market = False
enter_long_conditions = [df["do_predict"] == 1, df["min"] >= self.entry_thr.value]
if enter_long_conditions:
@ -415,6 +494,7 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
trade_candle = trade_candle.squeeze()
if dataframe["do_predict"].iloc[-1] != 1:
self.panic_market = True
return f"OOD_{trade.enter_tag}_Exit"
time_alpha = (1 + current_profit / (self.minimal_roi[0] - self.stoploss))