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fix ta-lib issue with simultaneous method access

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This commit is contained in:
robcaulk 2022-06-02 14:37:40 +02:00
parent 4ac6ef2972
commit b37c31cc21
1 changed files with 132 additions and 107 deletions
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239
freqtrade/templates/FreqaiExampleStrategy.py
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@ -49,9 +49,10 @@ class FreqaiExampleStrategy(IStrategy):
startup_candle_count: int = 300 startup_candle_count: int = 300
can_short = False can_short = False
linear_roi_offset = DecimalParameter(0.00, 0.02, default=0.005, space='sell', linear_roi_offset = DecimalParameter(
optimize=False, load=True) 0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
max_roi_time_long = IntParameter(0, 800, default=400, space='sell', optimize=False, load=True) )
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def informative_pairs(self): def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist() whitelist_pairs = self.dp.current_whitelist()
@ -85,89 +86,103 @@ class FreqaiExampleStrategy(IStrategy):
:coin: the name of the coin which will modify the feature names. :coin: the name of the coin which will modify the feature names.
""" """
if informative is None: with self.model.bridge.lock:
informative = self.dp.get_pair_dataframe(pair, tf) if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods # 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"]:
t = int(t) t = int(t)
informative['%-' + coin + "rsi-period_" + str(t)] = ta.RSI(informative, timeperiod=t) informative["%-" + coin + "rsi-period_" + str(t)] = ta.RSI(
informative['%-' + coin + "mfi-period_" + str(t)] = ta.MFI(informative, timeperiod=t) informative, timeperiod=t
informative['%-' + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t) )
informative[coin + "20sma-period_" + str(t)] = ta.SMA(informative, timeperiod=t) informative["%-" + coin + "mfi-period_" + str(t)] = ta.MFI(
informative[coin + "21ema-period_" + str(t)] = ta.EMA(informative, timeperiod=t) informative, timeperiod=t
informative['%-' + coin + "close_over_20sma-period_" + )
str(t)] = (informative["close"] / informative["%-" + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t)
informative[coin + "20sma-period_" + str(t)]) informative[coin + "20sma-period_" + str(t)] = ta.SMA(informative, timeperiod=t)
informative[coin + "21ema-period_" + str(t)] = ta.EMA(informative, timeperiod=t)
informative["%-" + coin + "close_over_20sma-period_" + str(t)] = (
informative["close"] / informative[coin + "20sma-period_" + str(t)]
)
informative['%-' + coin + "mfi-period_" + str(t)] = ta.MFI(informative, timeperiod=t) informative["%-" + coin + "mfi-period_" + str(t)] = ta.MFI(
informative, timeperiod=t
)
informative[coin + "ema21-period_" + str(t)] = ta.EMA(informative, timeperiod=t) informative[coin + "ema21-period_" + str(t)] = ta.EMA(informative, timeperiod=t)
informative[coin + "sma20-period_" + str(t)] = ta.SMA(informative, timeperiod=t) informative[coin + "sma20-period_" + str(t)] = ta.SMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=t, bollinger = qtpylib.bollinger_bands(
stds=2.2) qtpylib.typical_price(informative), window=t, stds=2.2
informative[coin + "bb_lowerband-period_" + str(t)] = bollinger["lower"] )
informative[coin + "bb_middleband-period_" + str(t)] = bollinger["mid"] informative[coin + "bb_lowerband-period_" + str(t)] = bollinger["lower"]
informative[coin + "bb_upperband-period_" + str(t)] = bollinger["upper"] informative[coin + "bb_middleband-period_" + str(t)] = bollinger["mid"]
informative['%-' + coin + "bb_width-period_" + str(t)] = ( informative[coin + "bb_upperband-period_" + str(t)] = bollinger["upper"]
informative[coin + "bb_upperband-period_" + str(t)] - informative["%-" + coin + "bb_width-period_" + str(t)] = (
informative[coin + "bb_lowerband-period_" + str(t)] informative[coin + "bb_upperband-period_" + str(t)]
) / informative[coin + "bb_middleband-period_" + str(t)] - informative[coin + "bb_lowerband-period_" + str(t)]
informative['%-' + coin + "close-bb_lower-period_" + str(t)] = ( ) / informative[coin + "bb_middleband-period_" + str(t)]
informative["close"] / informative[coin + "bb_lowerband-period_" + str(t)] informative["%-" + coin + "close-bb_lower-period_" + str(t)] = (
) informative["close"] / informative[coin + "bb_lowerband-period_" + str(t)]
)
informative['%-' + coin + "roc-period_" + str(t)] = ta.ROC(informative, timeperiod=t) informative["%-" + coin + "roc-period_" + str(t)] = ta.ROC(
informative['%-' + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t) informative, timeperiod=t
)
informative["%-" + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t)
macd = ta.MACD(informative, timeperiod=t) macd = ta.MACD(informative, timeperiod=t)
informative['%-' + coin + "macd-period_" + str(t)] = macd["macd"] informative["%-" + coin + "macd-period_" + str(t)] = macd["macd"]
informative['%-' + coin + "relative_volume-period_" + str(t)] = ( informative["%-" + coin + "relative_volume-period_" + str(t)] = (
informative["volume"] / informative["volume"].rolling(t).mean() informative["volume"] / informative["volume"].rolling(t).mean()
) )
informative['%-' + coin + "pct-change"] = informative["close"].pct_change() informative["%-" + coin + "pct-change"] = informative["close"].pct_change()
informative['%-' + coin + "raw_volume"] = informative["volume"] informative["%-" + coin + "raw_volume"] = informative["volume"]
informative['%-' + coin + 'raw_price'] = informative['close'] informative["%-" + coin + "raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith('%')] indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data # 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"]["shift"] + 1):
if n == 0: if n == 0:
continue continue
informative_shift = informative[indicators].shift(n) informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n)) informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1) informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, 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"]] skip_columns = [
df = df.drop(columns=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 # Add generalized indicators here (because in live, it will call this
# indicators during training). Notice how we ensure not to add them multiple times # function to populate indicators during training). Notice how we ensure not to
if pair == metadata['pair'] and tf == self.timeframe: # add them multiple times
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 if pair == metadata["pair"] and tf == self.timeframe:
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
return df return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"] self.freqai_info = self.config["freqai"]
self.pair = metadata['pair'] self.pair = metadata["pair"]
# the following loops are necessary for building the features # the following loops are necessary for building the features
# indicated by the user in the configuration file. # indicated by the user in the configuration file.
# All indicators must be populated by populate_any_indicators() for live functionality # All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly. # to work correctly.
for tf in self.freqai_info["timeframes"]: for tf in self.freqai_info["timeframes"]:
dataframe = self.populate_any_indicators(metadata, self.pair, dataframe.copy(), tf, dataframe = self.populate_any_indicators(
coin=self.pair.split("/")[0] + "-") metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-"
)
for pair in self.freqai_info["corr_pairlist"]: for pair in self.freqai_info["corr_pairlist"]:
if metadata['pair'] in pair: if metadata["pair"] in pair:
continue # do not include whitelisted pair twice if it is in corr_pairlist continue # do not include whitelisted pair twice if it is in corr_pairlist
dataframe = self.populate_any_indicators( dataframe = self.populate_any_indicators(
metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
@ -189,38 +204,28 @@ class FreqaiExampleStrategy(IStrategy):
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [ enter_long_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"]]
df['do_predict'] == 1,
df['prediction'] > df["target_roi"]
]
if enter_long_conditions: if enter_long_conditions:
df.loc[reduce(lambda x, y: x & y, df.loc[
enter_long_conditions), ["enter_long", "enter_tag"]] = (1, 'long') reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [ enter_short_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"]]
df['do_predict'] == 1,
df['prediction'] < df["sell_roi"]
]
if enter_short_conditions: if enter_short_conditions:
df.loc[reduce(lambda x, y: x & y, df.loc[
enter_short_conditions), ["enter_short", "enter_tag"]] = (1, 'short') reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [ exit_long_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"] * 0.25]
df['do_predict'] == 1,
df['prediction'] < df['sell_roi'] * 0.25
]
if exit_long_conditions: if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [ exit_short_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"] * 0.25]
df['do_predict'] == 1,
df['prediction'] > df['target_roi'] * 0.25
]
if exit_short_conditions: if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
@ -229,19 +234,20 @@ class FreqaiExampleStrategy(IStrategy):
def get_ticker_indicator(self): def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1]) return int(self.config["timeframe"][:-1])
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, def custom_exit(
current_profit, **kwargs): self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc) trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
trade_candle = dataframe.loc[(dataframe['date'] == trade_date)] trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
if trade_candle.empty: if trade_candle.empty:
return None return None
trade_candle = trade_candle.squeeze() trade_candle = trade_candle.squeeze()
follow_mode = self.config.get('freqai', {}).get('follow_mode', False) follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode: if not follow_mode:
pair_dict = self.model.bridge.data_drawer.pair_dict pair_dict = self.model.bridge.data_drawer.pair_dict
@ -250,53 +256,63 @@ class FreqaiExampleStrategy(IStrategy):
entry_tag = trade.enter_tag entry_tag = trade.enter_tag
if 'prediction' + entry_tag not in pair_dict[pair]: if "prediction" + entry_tag not in pair_dict[pair]:
with self.model.bridge.lock: with self.model.bridge.lock:
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction']) pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["prediction"])
if not follow_mode: if not follow_mode:
self.model.bridge.data_drawer.save_drawer_to_disk() self.model.bridge.data_drawer.save_drawer_to_disk()
else: else:
self.model.bridge.data_drawer.save_follower_dict_to_dist() self.model.bridge.data_drawer.save_follower_dict_to_dist()
else: else:
if pair_dict[pair]['prediction' + entry_tag] > 0: if pair_dict[pair]["prediction" + entry_tag] > 0:
roi_price = abs(trade_candle['prediction']) roi_price = abs(trade_candle["prediction"])
else: else:
with self.model.bridge.lock: with self.model.bridge.lock:
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction']) pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["prediction"])
if not follow_mode: if not follow_mode:
self.model.bridge.data_drawer.save_drawer_to_disk() self.model.bridge.data_drawer.save_drawer_to_disk()
else: else:
self.model.bridge.data_drawer.save_follower_dict_to_dist() self.model.bridge.data_drawer.save_follower_dict_to_dist()
roi_price = abs(trade_candle['prediction']) roi_price = abs(trade_candle["prediction"])
roi_time = self.max_roi_time_long.value roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) / roi_decay = roi_price * (
(roi_time * 60)) 1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
)
if roi_decay < 0: if roi_decay < 0:
roi_decay = self.linear_roi_offset.value roi_decay = self.linear_roi_offset.value
else: else:
roi_decay += self.linear_roi_offset.value roi_decay += self.linear_roi_offset.value
if current_profit > roi_decay: if current_profit > roi_decay:
return 'roi_custom_win' return "roi_custom_win"
if current_profit < -roi_decay: if current_profit < -roi_decay:
return 'roi_custom_loss' return "roi_custom_loss"
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, def confirm_trade_exit(
rate: float, time_in_force: str, exit_reason: str, self,
current_time, **kwargs) -> bool: pair: str,
trade: Trade,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
exit_reason: str,
current_time,
**kwargs
) -> bool:
entry_tag = trade.enter_tag entry_tag = trade.enter_tag
follow_mode = self.config.get('freqai', {}).get('follow_mode', False) follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode: if not follow_mode:
pair_dict = self.model.bridge.data_drawer.pair_dict pair_dict = self.model.bridge.data_drawer.pair_dict
else: else:
pair_dict = self.model.bridge.data_drawer.follower_dict pair_dict = self.model.bridge.data_drawer.follower_dict
with self.model.bridge.lock: with self.model.bridge.lock:
pair_dict[pair]['prediction' + entry_tag] = 0 pair_dict[pair]["prediction" + entry_tag] = 0
if not follow_mode: if not follow_mode:
self.model.bridge.data_drawer.save_drawer_to_disk() self.model.bridge.data_drawer.save_drawer_to_disk()
else: else:
@ -304,18 +320,27 @@ class FreqaiExampleStrategy(IStrategy):
return True return True
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, def confirm_trade_entry(
time_in_force: str, current_time, entry_tag, self,
side: str, **kwargs) -> bool: pair: str,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
current_time,
entry_tag,
side: str,
**kwargs
) -> bool:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze() last_candle = df.iloc[-1].squeeze()
if side == 'long': if side == "long":
if rate > (last_candle['close'] * (1 + 0.0025)): if rate > (last_candle["close"] * (1 + 0.0025)):
return False return False
else: else:
if rate < (last_candle['close'] * (1 - 0.0025)): if rate < (last_candle["close"] * (1 - 0.0025)):
return False return False
return True return True