Timeframe
N/A
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 3.0%, 30m: 2.0%, 60m: 1.0%, 600m: 0.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
11
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import logging
import time
from pathlib import Path
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np # noqa
import pandas as pd # noqa
import talib as ta
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from stable_baselines3 import A2C
from stable_baselines3.ppo.ppo import PPO
import predict
logger = logging.getLogger(__name__)
COLUMNS_FILTER = [
'date',
'open',
'close',
'high',
'low',
'volume',
'buy',
'sell',
'buy_tag',
'exit_tag',
]
class FreqGymNormalized(IStrategy):
# # If you've used SimpleROIEnv then use this minimal_roi
# minimal_roi = {
# "720": -10,
# "600": 0.00001,
# "60": 0.01,
# "30": 0.02,
# "0": 0.03
# }
minimal_roi = {"0": 100}
stoploss = -0.99
# Trailing stop:
trailing_stop = False
trailing_stop_positive = 0.001
trailing_stop_positive_offset = 0.017
trailing_only_offset_is_reached = True
ticker_interval = '5m'
use_sell_signal = True
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
startup_candle_count: int = 200
model = None
window_size = None
timeperiods = [7, 14, 21]
def __init__(self, config: dict) -> None:
super().__init__(config)
try:
# get the file that starts with "best_model_" in the models/ directory
# list files in the directory
# files = Path('models/').glob('final_model_*')
# get the first file
# model_file = next(files)
model_file = Path(
'models/best_model_FreqGymNormalized_FreqtradeEnv_A2C_20220318_115558.zip'
)
assert model_file.exists(), f'Model file "{model_file}" does not exist.'
self.model = A2C.load(
str(model_file)
) # Note: Make sure you use the same policy as the one used to train
self.window_size = self.model.observation_space.shape[0]
except Exception as e:
logger.exception(f'Could not load model: {e}')
else:
logger.info(f'Loaded model: {model_file}')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
logger.info(f'Calculating TA indicators for {metadata["pair"]}')
# Plus Directional Indicator / Movement
dataframe['plus_di'] = normalize(ta.PLUS_DI(dataframe), 0, 100)
# # Minus Directional Indicator / Movement
dataframe['minus_di'] = normalize(ta.MINUS_DI(dataframe), 0, 100)
# Ultimate Oscillator
dataframe['uo'] = normalize(ta.ULTOSC(dataframe), 0, 100)
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = normalize(hilbert['sine'], -1, 1)
dataframe['htleadsine'] = normalize(hilbert['leadsine'], -1, 1)
# BOP Balance Of Power
dataframe['bop'] = normalize(ta.BOP(dataframe), -1, 1)
# STOCH - Stochastic
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = normalize(stoch['slowk'], 0, 100)
dataframe['slowd'] = normalize(stoch['slowd'], 0, 100)
# STOCHF - Stochastic Fast
stochf = ta.STOCHF(dataframe)
dataframe['fastk'] = normalize(stochf['fastk'], 0, 100)
dataframe['fastk'] = normalize(stochf['fastk'], 0, 100)
# Bollinger Bands
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb2_lower_gt_close'] = bollinger2['lower'].gt(dataframe['close']).astype('int')
dataframe['bb3_lower_gt_close'] = bollinger3['lower'].gt(dataframe['close']).astype('int')
for period in self.timeperiods:
# ADX Average Directional Movement Index
dataframe[f'adx_{period}'] = normalize(ta.ADX(dataframe, timeperiod=period), 0, 100)
# Aroon, Aroon Oscillator
aroon = ta.AROON(dataframe, timeperiod=period)
dataframe[f'aroonup_{period}'] = normalize(aroon['aroonup'], 0, 100)
dataframe[f'aroondown_{period}'] = normalize(aroon['aroondown'], 0, 100)
dataframe[f'aroonosc_{period}'] = normalize(
ta.AROONOSC(dataframe, timeperiod=period), -100, 100
)
# CMO Chande Momentum Oscillator
dataframe[f'cmo_{period}'] = normalize(ta.CMO(dataframe, timeperiod=period), -100, 100)
# DX Directional Movement Index
dataframe[f'dx_{period}'] = normalize(ta.DX(dataframe, timeperiod=period), 0, 100)
# MFI Money Flow Index
dataframe[f'mfi_{period}'] = normalize(ta.MFI(dataframe, timeperiod=period), 0, 100)
# MINUS_DI Minus Directional Indicator
dataframe[f'minus_di_{period}'] = normalize(
ta.MINUS_DI(dataframe, timeperiod=period), 0, 100
)
# PLUS_DI Plus Directional Indicator
dataframe[f'plus_di_{period}'] = normalize(
ta.PLUS_DI(dataframe, timeperiod=period), 0, 100
)
# Williams %R
dataframe[f'willr_{period}'] = normalize(
ta.WILLR(dataframe, timeperiod=period), -100, 0
)
# RSI
dataframe[f'rsi_{period}'] = normalize(ta.RSI(dataframe, timeperiod=period), 0, 100)
# Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe[f'rsi_{period}'] - 50)
dataframe[f'fisher_rsi_{period}'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
dataframe[f'fisher_rsi_{period}'] = normalize(dataframe[f'fisher_rsi_{period}'], -1, 1)
# STOCHRSI - Stochastic Relative Strength Index
stoch_rsi = ta.STOCHRSI(dataframe, timeperiod=period)
dataframe[f'stochrsi_k_{period}'] = normalize(stoch_rsi['fastk'], 0, 100)
dataframe[f'stochrsi_d_{period}'] = normalize(stoch_rsi['fastd'], 0, 100)
# # CORREL - Pearson's Correlation Coefficient (r)
# dataframe[f'correl_{period}'] = normalize(ta.CORREL(dataframe, timeperiod=period), -1, 1) # this is buggy
# LINEARREG_ANGLE - Linear Regression Angle
dataframe[f'linangle_{period}'] = normalize(
ta.LINEARREG_ANGLE(dataframe, timeperiod=period), -90, 90
)
indicators = dataframe[dataframe.columns[~dataframe.columns.isin(COLUMNS_FILTER)]]
assert all(indicators.max() < 1.00001) and all(
indicators.min() > -0.00001
), "Error, values are not normalized!"
logger.info(f'{metadata["pair"]} - indicators populated!')
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
# dataframe['buy'] = self.rl_model_predict(dataframe)
logger.info(f'Populating buy signal for {metadata["pair"]}')
action = self.rl_model_predict(dataframe)
dataframe['buy'] = (action == 1).astype('int')
logger.info(f'{metadata["pair"]} - buy signal populated!')
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
logger.info(f'Populating sell signal for {metadata["pair"]}')
action = self.rl_model_predict(dataframe)
dataframe['sell'] = (action == 2).astype('int')
logger.info(f'{metadata["pair"]} - sell signal populated!')
return dataframe
def rl_model_predict(self, dataframe: pd.DataFrame) -> pd.DataFrame:
output = pd.DataFrame(np.zeros((len(dataframe), 1)))
indicators = (
dataframe[dataframe.columns[~dataframe.columns.isin(COLUMNS_FILTER)]]
.fillna(0)
.to_numpy()
)
indicators2 = dataframe[dataframe.columns[~dataframe.columns.isin(COLUMNS_FILTER)]].fillna(
0
)
# convert above code to rolling
# self.model.predict expects a numpy array of shape (batch_size, seq_len)
# t1 = time.time()
# second_output = indicators2.rolling(
# window=self.window_size, min_periods=self.window_size
# ).apply(lambda x: self.predict(x))
# logger.info(f'Elapsed time rolling: {time.time() - t1}')
# TODO: This is slow and ugly, must use .rolling
t1 = time.time()
for window in range(self.window_size, len(dataframe)):
start = window - self.window_size
end = window
observation = indicators[start:end]
res, _ = predict.predict(observation, deterministic=True)
output.loc[end] = res
logger.info(f'Elapsed time default: {time.time() - t1}')
return output
def predict(self, x: pd.Series):
logger.info(f'Predicting action for {x}, type: {type(x)}')
return predict.predict(x, deterministic=True)
def normalize(data, min_value, max_value):
"""
Given a list of numbers, normalize each number by subtracting the minimum and dividing by the
maximum
:param data: The data to be normalized
:param min_value: the minimum value in the data
:param max_value: The maximum value in the column
:return: The normalized data
"""
return (data - min_value) / (max_value - min_value)