Timeframe
15m
Direction
Long Only
Stoploss
-5.0%
Trailing Stop
No
ROI
0m: 5.0%, 30m: 3.0%, 60m: 2.0%
Interface Version
3
Startup Candles
N/A
Indicators
2
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,
AnnotationType,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib
class SimpleFibonacciStrategy(IStrategy):
"""
简化的斐波那契策略 - 用于测试
"""
# Strategy interface version
INTERFACE_VERSION = 3
# 策略时间框架 - 15分钟
timeframe = "15m"
# 是否支持做空
can_short: bool = False
# 最小ROI设置
minimal_roi = {
"60": 0.02, # 1小时后2%收益
"30": 0.03, # 30分钟后3%收益
"0": 0.05 # 立即5%收益
}
# 止损设置
stoploss = -0.05 # 5%止损
# 只处理新K线
process_only_new_candles = True
# 策略参数
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# 策略启动所需的K线数量
startup_candle_count: int = 30
# 订单类型
order_types = {
"entry": "limit",
"exit": "limit",
"stoploss": "market",
"stoploss_on_exchange": False
}
# 订单时间
order_time_in_force = {
"entry": "GTC",
"exit": "GTC"
}
def informative_pairs(self):
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
计算技术指标
"""
# 计算RSI
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
# 计算斐波那契回归水平 - 使用固定周期
period = 30
dataframe["highest"] = dataframe["high"].rolling(window=period).max()
dataframe["lowest"] = dataframe["low"].rolling(window=period).min()
# 计算价格区间
dataframe["price_range"] = dataframe["highest"] - dataframe["lowest"]
# 计算斐波那契回归水平
dataframe["fib_0.618"] = dataframe["highest"] - 0.618 * dataframe["price_range"]
dataframe["fib_0.382"] = dataframe["highest"] - 0.382 * dataframe["price_range"]
# 计算EMA
dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
简化的买入信号
"""
dataframe.loc[
(
# 价格接近0.618斐波那契回归位
(dataframe["close"] <= dataframe["fib_0.618"] * 1.05) & # 允许5%的误差
(dataframe["close"] >= dataframe["fib_0.618"] * 0.95) &
# RSI显示超卖状态
(dataframe["rsi"] <= 40) &
# 确保有足够的数据
(dataframe["volume"] > 0) &
(dataframe["price_range"] > 0)
),
"enter_long"
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
简化的卖出信号
"""
dataframe.loc[
(
# 价格接近0.382斐波那契回归位
(dataframe["close"] >= dataframe["fib_0.382"] * 0.95) & # 允许5%的误差
(dataframe["close"] <= dataframe["fib_0.382"] * 1.05) &
# RSI显示超买状态
(dataframe["rsi"] >= 60) &
# 确保有足够的数据
(dataframe["volume"] > 0) &
(dataframe["price_range"] > 0)
),
"exit_long"
] = 1
return dataframe