Timeframe
5m
Direction
Long Only
Stoploss
-13.0%
Trailing Stop
No
ROI
0m: 2.0%, 120m: 1.0%
Interface Version
N/A
Startup Candles
120
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime
import talib.abstract as ta # TA-Lib 抽象接口(可直接对 DataFrame 使用)
import pandas_ta as pta # pandas_ta 指标库,这里用到 cti
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce # 组合多个条件用(逻辑或)
class SampleStrategy1(IStrategy):
# ROI 表:持仓 0 分钟起要求 2% 盈利即可触发 ROI 卖出;持仓满 120 分钟后只要 1%
minimal_roi = {
"0": 0.02,
"120": 0.01
}
timeframe = '5m' # 策略运行在 5 分钟线
process_only_new_candles = True # 只在新 K 线计算,省 CPU
startup_candle_count = 120 # 至少准备 120 根 K,用于指标热身
position_adjustment_enable = True # 启用“仓位调整”(分批加减仓)
# 订单类型设定
order_types = {
'entry': 'market', # 入场用市价
'exit': 'market', # 出场用市价
'emergency_exit': 'market',
'force_entry': 'market',
'force_exit': "market",
'stoploss': 'market', # 止损用市价
'stoploss_on_exchange': False, # 不在交易所挂实体止损单(本地管)
# 下面两项仅在 stoploss_on_exchange=True 时生效,这里相当于预留
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_market_ratio': 0.99
}
stoploss = -0.13 # 全局硬止损:-13%
# —— 超参定义(可被 Hyperopt 优化)——
is_optimize_32 = True
buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32)
buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32)
buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32)
buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32)
# —— 初始下单金额控制:只用建议仓位的 70% 开仓 ——
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
leverage: float, entry_tag: str, side: str,
**kwargs) -> float:
return proposed_stake * 0.7 # 预留 30% 弹药以便后续加仓
# —— 仓位调整(分批/加减仓)——
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: float, max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> float:
count_of_exits = trade.nr_of_successful_exits
# 条件1:若当前浮盈 ≥ 2%,且从未分批减仓过 → 减仓 50%
if current_profit >= 0.02 and count_of_exits == 0:
return -0.5 * trade.stake_amount # 负值=减仓金额(按初始 stake 的一半)
count_of_entries = trade.nr_of_successful_entries
filled_entries = trade.select_filled_orders(trade.entry_side) # 历史入场订单
initial_stake = filled_entries[0].cost # 第一次入场的花费(70%)
# 条件2:若浮亏 < -5% 且仅入场过一次 → 补仓到满仓(把剩余 30% 补上)
if current_profit < -0.05 and count_of_entries == 1 :
return initial_stake / 0.7 * 0.3 # 初始 70% 反推总额,再乘 30% 得到补仓额
return None # 其他情况不调整
# —— 计算技术指标 ——
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) # 15 期简单均线
dataframe['cti'] = pta.cti(dataframe["close"], length=20) # CTI(20):相关性趋势指标,负值偏超卖
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # 标准 RSI(14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) # 快 RSI(4):更敏感
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # 慢 RSI(20):趋势性
# STOCHF:快速随机指标,这里只用 fastk(0–100)
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) # 建议改成关键词参数以免歧义
dataframe['fastk'] = stoch_fast['fastk']
return dataframe
# —— 入场条件 ——
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'enter_tag'] = '' # 记录触发的买入标签
# buy_1 组合信号:
buy_1 = (
(dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & # 慢 RSI 走低(捕捉回撤)
(dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) & # 快 RSI 低于阈值(短期超卖)
(dataframe['rsi'] > self.buy_rsi_32.value) & # 但整体 RSI 不能太弱
(dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) & # 价格低于 SMA15 的某比例
(dataframe['cti'] < self.buy_cti_32.value) # CTI 足够负,倾向反弹
)
conditions.append(buy_1)
dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' # 打标签方便回测分析
# 任一买入条件满足 → 标记 enter_long = 1(Freqtrade 会据此下单)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'enter_long'] = 1
return dataframe
# —— 出场条件 ——
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out') # 示例:可自定义出场信号
dataframe['exit_long'] = 0
dataframe['exit_tag'] = '' # 这里不主动给卖出信号
# 实际卖出由 ROI 表 / 全局止损 / adjust_trade_position 决定
return dataframe