Timeframe
1h
Direction
Long Only
Stoploss
-100.0%
Trailing Stop
No
ROI
0m: 100.0%
Interface Version
3
Startup Candles
N/A
Indicators
0
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
this is an example class, implementing a PSAR based trailing stop loss you are supposed to take the `custom_stoploss()` and `populate_indicators()` parts and adapt it to your own strategy
freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import (
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IStrategy,
IntParameter,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import logging
logger = logging.getLogger(__name__)
class LongSpotStrategy(IStrategy):
INTERFACE_VERSION = 3
timeframe = "1h"
minimal_roi = {"0": 1}
stoploss = -1
can_short = False
trailing_stop = False
process_only_new_candles = True
startup_candle_count: int = 30
# Hyperparameters
entry_sharpe_signal = DecimalParameter(0.0, 1.0, default=0.5, space="buy")
exit_sharpe_signal = DecimalParameter(0.0, 1.0, default=0.5, space="sell")
volume_threshold = IntParameter(1, 100, default=1, space="buy_sell")
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
url_1hr = 'https://raw.githubusercontent.com/spmcelrath/images/main/bitcoin-bitcoin-sharpe-signal-short-1hr.csv'
url_1d = 'https://raw.githubusercontent.com/spmcelrath/images/main/bitcoin-bitcoin-sharpe-signal-short-1d.csv'
# Load Sharpe signals from URLs
sharpe_signals_1hr = pd.read_csv(url_1hr, index_col='timestamp', parse_dates=True)
sharpe_signals_1d = pd.read_csv(url_1d, index_col='timestamp', parse_dates=True)
# Merge the signals based on the timeframe
if self.timeframe == "1h":
sharpe_signals = sharpe_signals_1hr
elif self.timeframe == "1d":
sharpe_signals = sharpe_signals_1d
else:
raise ValueError(f"Unsupported timeframe: {self.timeframe}")
# Rename 'value' column to 'sharpe_signal' for consistency
sharpe_signals.rename(columns={'value': 'sharpe_signal'}, inplace=True)
# Log the first few rows after renaming
logger.info(f"Sharpe signals after renaming column:\n{sharpe_signals.head()}")
# Resample to the same frequency as the OHLCV data
sharpe_signals = sharpe_signals.resample(self.timeframe).ffill()
# Join the signals with the dataframe and forward fill to match OHLCV data
dataframe.set_index('date', inplace=True)
dataframe = dataframe.join(sharpe_signals, how='left').fillna(method='ffill')
dataframe.reset_index(inplace=True)
# Log the dataframe columns and some sample data
logger.info(f"Dataframe columns after joining Sharpe signals: {dataframe.columns}")
logger.info(f"Sample dataframe data:\n{dataframe.head()}")
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe["sharpe_signal"] < self.entry_sharpe_signal.value)
& (dataframe["volume"] > self.volume_threshold.value)
),
"enter_long",
] = 1
logger.info(f"Entry signals: {dataframe[dataframe['enter_long'] == 1]}")
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe["sharpe_signal"], self.exit_sharpe_signal.value))
& (dataframe["volume"] > self.volume_threshold.value)
),
"exit_long",
] = 1
logger.info(f"Exit signals: {dataframe[dataframe['exit_long'] == 1]}")
return dataframe