fix ta-lib issue with simultaneous method access
This commit is contained in:
robcaulk
parent
4ac6ef2972
commit
b37c31cc21
|
|
@ -49,9 +49,10 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
startup_candle_count: int = 300
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(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)
|
||||
linear_roi_offset = DecimalParameter(
|
||||
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)
|
||||
|
||||
def informative_pairs(self):
|
||||
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.
|
||||
"""
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
with self.model.bridge.lock:
|
||||
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"]:
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods"]:
|
||||
|
||||
t = int(t)
|
||||
informative['%-' + coin + "rsi-period_" + str(t)] = ta.RSI(informative, timeperiod=t)
|
||||
informative['%-' + coin + "mfi-period_" + str(t)] = ta.MFI(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 + "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)])
|
||||
t = int(t)
|
||||
informative["%-" + coin + "rsi-period_" + str(t)] = ta.RSI(
|
||||
informative, timeperiod=t
|
||||
)
|
||||
informative["%-" + coin + "mfi-period_" + str(t)] = ta.MFI(
|
||||
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 + "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 + "sma20-period_" + str(t)] = ta.SMA(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)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(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_upperband-period_" + str(t)] = bollinger["upper"]
|
||||
informative['%-' + coin + "bb_width-period_" + str(t)] = (
|
||||
informative[coin + "bb_upperband-period_" + str(t)] -
|
||||
informative[coin + "bb_lowerband-period_" + str(t)]
|
||||
) / informative[coin + "bb_middleband-period_" + str(t)]
|
||||
informative['%-' + coin + "close-bb_lower-period_" + str(t)] = (
|
||||
informative["close"] / informative[coin + "bb_lowerband-period_" + str(t)]
|
||||
)
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
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_upperband-period_" + str(t)] = bollinger["upper"]
|
||||
informative["%-" + coin + "bb_width-period_" + str(t)] = (
|
||||
informative[coin + "bb_upperband-period_" + str(t)]
|
||||
- informative[coin + "bb_lowerband-period_" + str(t)]
|
||||
) / informative[coin + "bb_middleband-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 + "adx-period_" + str(t)] = ta.ADX(informative, window=t)
|
||||
informative["%-" + coin + "roc-period_" + str(t)] = ta.ROC(
|
||||
informative, timeperiod=t
|
||||
)
|
||||
informative["%-" + coin + "adx-period_" + str(t)] = ta.ADX(informative, window=t)
|
||||
|
||||
macd = ta.MACD(informative, timeperiod=t)
|
||||
informative['%-' + coin + "macd-period_" + str(t)] = macd["macd"]
|
||||
macd = ta.MACD(informative, timeperiod=t)
|
||||
informative["%-" + coin + "macd-period_" + str(t)] = macd["macd"]
|
||||
|
||||
informative['%-' + coin + "relative_volume-period_" + str(t)] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
informative["%-" + coin + "relative_volume-period_" + str(t)] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
informative['%-' + coin + "pct-change"] = informative["close"].pct_change()
|
||||
informative['%-' + coin + "raw_volume"] = informative["volume"]
|
||||
informative['%-' + coin + 'raw_price'] = informative['close']
|
||||
informative["%-" + coin + "pct-change"] = informative["close"].pct_change()
|
||||
informative["%-" + coin + "raw_volume"] = informative["volume"]
|
||||
informative["%-" + coin + "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"]["shift"] + 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)
|
||||
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):
|
||||
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)
|
||||
|
||||
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)
|
||||
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 pair == metadata['pair'] and tf == self.timeframe:
|
||||
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
|
||||
# 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 pair == metadata["pair"] and tf == self.timeframe:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
self.pair = metadata['pair']
|
||||
self.pair = metadata["pair"]
|
||||
|
||||
# the following loops are necessary for building the features
|
||||
# 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"]:
|
||||
dataframe = self.populate_any_indicators(metadata, self.pair, dataframe.copy(), tf,
|
||||
coin=self.pair.split("/")[0] + "-")
|
||||
dataframe = self.populate_any_indicators(
|
||||
metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-"
|
||||
)
|
||||
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
|
||||
dataframe = self.populate_any_indicators(
|
||||
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:
|
||||
|
||||
enter_long_conditions = [
|
||||
df['do_predict'] == 1,
|
||||
df['prediction'] > df["target_roi"]
|
||||
]
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"]]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y,
|
||||
enter_long_conditions), ["enter_long", "enter_tag"]] = (1, 'long')
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [
|
||||
df['do_predict'] == 1,
|
||||
df['prediction'] < df["sell_roi"]
|
||||
]
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"]]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y,
|
||||
enter_short_conditions), ["enter_short", "enter_tag"]] = (1, 'short')
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [
|
||||
df['do_predict'] == 1,
|
||||
df['prediction'] < df['sell_roi'] * 0.25
|
||||
]
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"] * 0.25]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [
|
||||
df['do_predict'] == 1,
|
||||
df['prediction'] > df['target_roi'] * 0.25
|
||||
]
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"] * 0.25]
|
||||
if exit_short_conditions:
|
||||
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):
|
||||
return int(self.config["timeframe"][:-1])
|
||||
|
||||
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate,
|
||||
current_profit, **kwargs):
|
||||
def custom_exit(
|
||||
self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
|
||||
):
|
||||
|
||||
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_candle = dataframe.loc[(dataframe['date'] == trade_date)]
|
||||
trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
|
||||
trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
|
||||
|
||||
if trade_candle.empty:
|
||||
return None
|
||||
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:
|
||||
pair_dict = self.model.bridge.data_drawer.pair_dict
|
||||
|
|
@ -250,53 +256,63 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
|
||||
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:
|
||||
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction'])
|
||||
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["prediction"])
|
||||
if not follow_mode:
|
||||
self.model.bridge.data_drawer.save_drawer_to_disk()
|
||||
else:
|
||||
self.model.bridge.data_drawer.save_follower_dict_to_dist()
|
||||
else:
|
||||
if pair_dict[pair]['prediction' + entry_tag] > 0:
|
||||
roi_price = abs(trade_candle['prediction'])
|
||||
if pair_dict[pair]["prediction" + entry_tag] > 0:
|
||||
roi_price = abs(trade_candle["prediction"])
|
||||
else:
|
||||
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:
|
||||
self.model.bridge.data_drawer.save_drawer_to_disk()
|
||||
else:
|
||||
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_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) /
|
||||
(roi_time * 60))
|
||||
roi_decay = roi_price * (
|
||||
1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
|
||||
)
|
||||
if roi_decay < 0:
|
||||
roi_decay = self.linear_roi_offset.value
|
||||
else:
|
||||
roi_decay += self.linear_roi_offset.value
|
||||
|
||||
if current_profit > roi_decay:
|
||||
return 'roi_custom_win'
|
||||
return "roi_custom_win"
|
||||
|
||||
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,
|
||||
rate: float, time_in_force: str, exit_reason: str,
|
||||
current_time, **kwargs) -> bool:
|
||||
def confirm_trade_exit(
|
||||
self,
|
||||
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
|
||||
follow_mode = self.config.get('freqai', {}).get('follow_mode', False)
|
||||
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
|
||||
if not follow_mode:
|
||||
pair_dict = self.model.bridge.data_drawer.pair_dict
|
||||
else:
|
||||
pair_dict = self.model.bridge.data_drawer.follower_dict
|
||||
|
||||
with self.model.bridge.lock:
|
||||
pair_dict[pair]['prediction' + entry_tag] = 0
|
||||
pair_dict[pair]["prediction" + entry_tag] = 0
|
||||
if not follow_mode:
|
||||
self.model.bridge.data_drawer.save_drawer_to_disk()
|
||||
else:
|
||||
|
|
@ -304,18 +320,27 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
|
||||
return True
|
||||
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time, entry_tag,
|
||||
side: str, **kwargs) -> bool:
|
||||
def confirm_trade_entry(
|
||||
self,
|
||||
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)
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
|
||||
if side == 'long':
|
||||
if rate > (last_candle['close'] * (1 + 0.0025)):
|
||||
if side == "long":
|
||||
if rate > (last_candle["close"] * (1 + 0.0025)):
|
||||
return False
|
||||
else:
|
||||
if rate < (last_candle['close'] * (1 - 0.0025)):
|
||||
if rate < (last_candle["close"] * (1 - 0.0025)):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
|
|
|||
Loading…
Reference in New Issue