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WhaleStrategy

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wentixiaogege/binance_trade/user_data/strategies/deepseek/strategy_whale_converted.py · first seen 2026-07-28 · repo updated 2026-06-21

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.12 has minimal roi trailing process only new candles: false startup candle count: 50 hyperopt
Indicators OBV SMA talib technical
Concepts trailing
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# strategy_whale.py
# 鲸鱼策略 - 捕捉大资金吸筹与派发
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd
pd.options.mode.chained_assignment = None
import technical.indicators as ftt
from functools import reduce
import numpy as np

class WhaleStrategy(IStrategy):
    INTERFACE_VERSION = 3
    '\n    鲸鱼策略 (Whale)\n    核心：捕捉大资金行为，识别吸筹/派发\n    指标：OBV、成交量突变、价格相对位置\n    入场：价格处于低位 + OBV走强 + 成交量放大\n    出场：价格处于高位 + OBV走弱 + 成交量萎缩\n    '  # 价格接近近期最低点的阈值（2%内）
    # 近期低点回溯周期
    buy_params = {'obv_ma_short': 5, 'obv_ma_long': 20, 'volume_ma_period': 20, 'volume_spike_factor': 1.5, 'price_low_percentile': 0.02, 'lookback_period': 20}  # 价格接近近期最高点的阈值
    sell_params = {'price_high_percentile': 0.98, 'obv_ma_short': 5, 'obv_ma_long': 20}
    minimal_roi = {'0': 0.1, '30': 0.05, '60': 0.02, '120': 0}
    stoploss = -0.12
    timeframe = '5m'
    startup_candle_count = 50
    process_only_new_candles = False
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    plot_config = {'main_plot': {}, 'subplots': {'OBV': {'obv': {}, 'obv_ma_short': {}, 'obv_ma_long': {}}, 'Volume': {'volume': {}, 'volume_ma': {}}}}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 能量潮 OBV
        dataframe['obv'] = ta.OBV(dataframe['close'], dataframe['volume'])
        dataframe['obv_ma_short'] = ta.SMA(dataframe['obv'], timeperiod=self.buy_params['obv_ma_short'])
        dataframe['obv_ma_long'] = ta.SMA(dataframe['obv'], timeperiod=self.buy_params['obv_ma_long'])
        # 成交量均线
        dataframe['volume_ma'] = ta.SMA(dataframe['volume'], timeperiod=self.buy_params['volume_ma_period'])
        # 价格相对位置：近期低点和高点
        dataframe['min_lookback'] = ta.MIN(dataframe['close'], timeperiod=self.buy_params['lookback_period'])
        dataframe['max_lookback'] = ta.MAX(dataframe['close'], timeperiod=self.buy_params['lookback_period'])
        # 价格接近低点标志
        dataframe['near_low'] = dataframe['close'] <= dataframe['min_lookback'] * (1 + self.buy_params['price_low_percentile'])
        # 价格接近高点标志
        dataframe['near_high'] = dataframe['close'] >= dataframe['max_lookback'] * self.sell_params['price_high_percentile']
        # 成交量突变
        dataframe['volume_spike'] = dataframe['volume'] > dataframe['volume_ma'] * self.buy_params['volume_spike_factor']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 吸筹条件：价格在低位，OBV短期均线上穿长期均线（量能先行），成交量放大
        conditions = [dataframe['near_low'] == True, qtpylib.crossed_above(dataframe['obv_ma_short'], dataframe['obv_ma_long']), dataframe['volume_spike'] == True]
        dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 派发条件：价格在高位，OBV短期均线下穿长期均线，成交量缩小（可选）
        conditions = [dataframe['near_high'] == True, qtpylib.crossed_below(dataframe['obv_ma_short'], dataframe['obv_ma_long'])]
        # 可附加成交量缩小条件：dataframe['volume'] < dataframe['volume_ma']
        dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
        return dataframe