This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/
Timeframe
1m
Direction
Long Only
Stoploss
-10000000.0%
Trailing Stop
No
ROI
0m: 10000000.0%
Interface Version
3
Startup Candles
N/A
Indicators
4
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
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple
from freqtrade.strategy import (
IStrategy,
Trade,
Order,
PairLocks,
informative, # @informative decorator
# Hyperopt Parameters
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IntParameter,
RealParameter,
# timeframe helpers
timeframe_to_minutes,
timeframe_to_next_date,
timeframe_to_prev_date,
# Strategy helper functions
merge_informative_pair,
stoploss_from_absolute,
stoploss_from_open,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib
class PriceVolume1MNoticeStrategy(IStrategy):
"""
This is a strategy template to get you started.
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Optimal timeframe for the strategy.
timeframe = "1m"
# Can this strategy go short?
can_short: bool = True
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 100000
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -100000
# Trailing stoploss
# trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Strategy parameters
# buy_rsi = IntParameter(10, 40, default=30, space="buy")
# sell_rsi = IntParameter(60, 90, default=70, space="sell")# Optional order type mapping.
order_types = {
"entry": "limit",
"exit": "limit",
"stoploss": "market",
"stoploss_on_exchange": False
}
# Optional order time in force.
order_time_in_force = {
"entry": "GTC",
"exit": "GTC"
}
@property
def plot_config(self):
return {
# Main plot indicators (Moving averages, ...)
"main_plot": {
"tema": {},
"sar": {"color": "white"},
},
"subplots": {
# Subplots - each dict defines one additional plot
"MACD": {
"macd": {"color": "blue"},
"macdsignal": {"color": "orange"},
},
"RSI": {
"rsi": {"color": "red"},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict):
dataframe = dataframe[-100:]
# 计算5分钟涨跌幅
recent = dataframe.iloc[-5:] # 最近5分钟
open_price = recent.iloc[0]['open']
close_price = recent.iloc[-1]['close']
pct_change = (close_price - open_price) / open_price
dataframe.loc[dataframe.index[-1], "price_change"] = pct_change
# 计算5分钟成交量
dataframe["5m_volume"] = dataframe["volume"].rolling(window=5).sum()
windows = 30
mean_vol = dataframe['5m_volume'].rolling(window=windows).mean()
std_vol = dataframe['5m_volume'].rolling(window=windows).std()
dataframe["z_score"] = (dataframe['5m_volume'] - mean_vol) / std_vol
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict):
dataframe = dataframe[-100:]
dataframe.loc[
(
(dataframe['price_change'] > 0.01) & # 价格上涨
(dataframe['z_score'] > 2) # Z-score大于1
),
'enter_long'] = 1
dataframe.loc[
(
(dataframe['price_change'] < -0.01) & # 价格下跌
(dataframe['z_score'] > 2) # Z-score小于-1
),
'enter_short'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict):
dataframe = dataframe[-100:]
return dataframe