大版本更新
This commit is contained in:
27942
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weex/stock_data.xlsx
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weex/stock_data.xlsx
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weex/test.py
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weex/test.py
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import datetime
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import pandas as pd
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import requests
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from loguru import logger
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# ================= 辅助函数 =================
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def is_bullish(candle):
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"""判断是否是阳线（开盘价 < 收盘价，即涨）"""
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return float(candle['open']) < float(candle['close'])
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def is_bearish(candle):
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"""判断是否是阴线（开盘价 > 收盘价，即跌）"""
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return float(candle['open']) > float(candle['close'])
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def check_signal(prev, curr):
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"""
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判断是否出现包住形态，返回信号类型和方向：
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1. 前跌后涨包住 -> 做多信号 (bear_bull_engulf)
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2. 前涨后跌包住 -> 做空信号 (bull_bear_engulf)
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3. 前涨后涨包住 -> 做多信号 (bull_bull_engulf)
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4. 前跌后跌包住 -> 做空信号 (bear_bear_engulf)
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"""
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p_open, p_close = float(prev['open']), float(prev['close']) # 前一笔
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c_open, c_close = float(curr['open']), float(curr['close']) # 当前一笔
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# 情况1：前跌后涨，且涨线包住前跌线 -> 做多信号
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if is_bullish(curr) and is_bearish(prev):
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if c_open <= p_close and c_close >= p_open:
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return "long", "bear_bull_engulf"
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# 情况2：前涨后跌，且跌线包住前涨线 -> 做空信号
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if is_bearish(curr) and is_bullish(prev):
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if c_open >= p_close and c_close <= p_open:
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return "short", "bull_bear_engulf"
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# # 情况3：前涨后涨，且后涨线包住前涨线 -> 做多信号
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# if is_bullish(curr) and is_bullish(prev):
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# if c_open < p_open and c_close > p_close:
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# return "long", "bull_bull_engulf"
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# # 情况4：前跌后跌，且后跌线包住前跌线 -> 做空信号
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# if is_bearish(curr) and is_bearish(prev):
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# if c_open > p_open and c_close < p_close:
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# return "short", "bear_bear_engulf"
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return None, None
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def simulate_trade(direction, entry_price, future_candles, take_profit_diff=30, stop_loss_diff=-10):
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"""
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模拟交易（逐根K线回测）
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使用价差来控制止盈止损：
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- 盈利达到 take_profit_diff 就止盈
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- 亏损达到 stop_loss_diff 就止损
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direction：信号类型
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entry_price：开盘价格
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future_candles：未来的行情数据
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"""
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for candle in future_candles:
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high, low, close = float(candle['high']), float(candle['low']), float(candle['close'])
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if direction == "long":
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# 做多：检查最高价是否达到止盈，最低价是否触及止损
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if high >= entry_price + take_profit_diff:
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return entry_price + take_profit_diff, take_profit_diff, candle['id'] # 止盈
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if low <= entry_price + stop_loss_diff:
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return entry_price + stop_loss_diff, stop_loss_diff, candle['id'] # 止损
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elif direction == "short":
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# 做空：检查最低价是否达到止盈，最高价是否触及止损
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if low <= entry_price - take_profit_diff:
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return entry_price - take_profit_diff, take_profit_diff, candle['id'] # 止盈
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if high >= entry_price - stop_loss_diff:
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return entry_price - stop_loss_diff, stop_loss_diff, candle['id'] # 止损
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# 如果未来都没触发，最后一根收盘平仓
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final_price = float(future_candles[-1]['close'])
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if direction == "long":
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diff_money = final_price - entry_price
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else:
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diff_money = entry_price - final_price
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return final_price, diff_money, future_candles[-1]['id']
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if __name__ == '__main__':
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zh_project = 0 # 累计盈亏
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all_trades = [] # 保存所有交易明细
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# 四种信号类型的统计
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signal_stats = {
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"bear_bull_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "涨包跌"},
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"bull_bear_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "跌包涨"},
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# "bull_bull_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "涨包涨"},
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# "bear_bear_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "跌包跌"}
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}
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headers = {
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'accept': 'application/json, text/plain, */*',
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'accept-language': 'zh-CN,zh;q=0.9',
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'appversion': '2.0.0',
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'bundleid': '',
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'cache-control': 'no-cache',
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'language': 'zh_CN',
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'origin': 'https://www.weeaxs.site',
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'pragma': 'no-cache',
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'priority': 'u=1, i',
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'referer': 'https://www.weeaxs.site/',
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'sec-ch-ua': '"Google Chrome";v="141", "Not?A_Brand";v="8", "Chromium";v="141"',
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'sec-ch-ua-mobile': '?0',
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'sec-ch-ua-platform': '"Windows"',
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'sec-fetch-dest': 'empty',
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'sec-fetch-mode': 'cors',
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'sec-fetch-site': 'cross-site',
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'sidecar': '0148e5be00be63a67540447fd47c4d0977aeb6aa56c3fde73f5841b534db3bf815',
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'terminalcode': '4a2cc45598d8543222359a255cdbef17',
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'terminaltype': '1',
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'traceid': 'mgkm2gayjxzpnfjpuv',
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'u-token': 'eyJhbGciOiJSUzI1NiJ9.eyJqdGkiOiI1NjBlNDg4Yy1jYzYxLTQzNTUtOTRjOC0yYWYwNTdkMGIyMzkxNDIyODc0MDY0IiwidWlkIjoidEQzQ1FIaFJUblVYcm5MNFNwckw3UT09Iiwic3ViIjoieXgyMDI1KioqKkBnbWFpbC5jb20iLCJpcCI6ImcwRzMydVRYUDF1ZGt3MjVFanZQenc9PSIsImRpZCI6Ii9DckplOTBGbURHREFCTnVzY0N5V0x0Nkk3R04yemRJS3RxZ0VPeU9HRk9nMk12cVptaUhFNmJ0YSt0OUgrcUEiLCJzdHMiOjAsImlhdCI6MTc2MDA4MDk5OSwiZXhwIjoxNzY3ODU2OTk5LCJwdXNoaWQiOiJvTmpMNm1ab2h4T203V3ZyZlIvcWdBPT0iLCJhdGwiOiIwIiwiaXNzIjoidXBleCJ9.64PHFr48cwtphejC-bFw8aLu9lx5jP81GBvrb0IHwBYM8EaWrcMU9VGT7zLL1mFYYUpedmTlS7EHzNvjuHNb8cUEEZGpAXKIGgQkyE48LrzhlQVASn3h0P7Wd9hWlLwcu1bOswo4Xgocecl0tpXaZVwAZccq4n13bqkEAouZLFM',
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'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36',
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'vs': 'v5b7p4i8zCMwEj9kSmzH7KMajbx3b6cS',
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'x-sig': 'e178e9659b38b516ed81ad87a8c5e741',
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'x-timestamp': '1760086517003',
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}
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datas = []
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klineId = None
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klineTime = None
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for i in range(500):
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print(i)
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params = {
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'languageType': '1',
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'sign': 'SIGN',
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'timeZone': 'string',
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'contractId': '10000002',
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'productCode': 'ethusdt',
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'priceType': 'LAST_PRICE',
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'klineType': 'MINUTE_15',
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'limit': '200',
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'nextKey.contractId': '10000002',
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'nextKey.klineId': klineId if klineId else '6593862509827714',
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'nextKey.klineTime': klineTime if klineTime else '1759851900000',
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}
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response = requests.get(
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'https://http-gateway2.janapw.com/api/v1/public/quote/v1/getKlineV2',
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params=params,
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headers=headers
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)
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klineId = response.json()["data"]["nextKey"]["klineId"]
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klineTime = response.json()["data"]["nextKey"]["klineTime"]
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for data in response.json()["data"]["dataList"]:
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# print(data)
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datas.append(
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{
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"open": data[3],
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"high": data[1],
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"low": data[2],
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"close": data[0],
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"id": data[4],
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}
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)
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# {'open': '4514.40', 'high': '4533.91', 'low': '4513.51', 'close': '4532.93', 'id': '1758003300000'}
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datas = sorted(datas, key=lambda x: x["id"])
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for data in datas:
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print(data)
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# 将列表转换为 DataFrame
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df = pd.DataFrame(datas)
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# 将 DataFrame 保存为 Excel 文件
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df.to_excel('stock_data.xlsx', index=False)
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print("数据已成功保存到 stock_data.xlsx 文件中。")
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daily_signals = 0 # 信号总数
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daily_wins = 0
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daily_profit = 0 # 价差总和
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# 遍历每根K线，寻找信号
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for idx in range(1, len(datas) - 2): # 留出未来K线
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prev, curr = datas[idx - 1], datas[idx] # 前一笔，当前一笔
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entry_candle = datas[idx + 1] # 下一根开盘价作为入场价
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future_candles = datas[idx + 2:] # 未来行情
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entry_open = float(entry_candle['open']) # 开仓价格
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direction, signal_type = check_signal(prev, curr) # 判断开仓方向和信号类型
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if direction and signal_type:
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daily_signals += 1
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exit_price, diff, exit_time = simulate_trade(direction, entry_open, future_candles, take_profit_diff=30,
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stop_loss_diff=-2)
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# 统计该信号类型的表现
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signal_stats[signal_type]["count"] += 1
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signal_stats[signal_type]["total_profit"] += diff
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if diff > 0:
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signal_stats[signal_type]["wins"] += 1
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daily_wins += 1
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daily_profit += diff
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# 将时间戳转换为本地时间
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local_time = datetime.datetime.fromtimestamp(int(entry_candle['id']) / 1000)
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formatted_time = local_time.strftime("%Y-%m-%d %H:%M:%S")
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# 保存交易详情
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all_trades.append(
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(
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f"{formatted_time}号",
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"做多" if direction == "long" else "做空",
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signal_stats[signal_type]["name"],
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entry_open,
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exit_price,
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diff,
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exit_time
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)
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)
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# ===== 输出每笔交易详情 =====
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logger.info("===== 每笔交易详情 =====")
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n = n1 = 0 # n = 总盈利，n1 = 总手续费
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for date, direction, signal_name, entry, exit, diff, end_time in all_trades:
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profit_amount = diff / entry * 10000 # 计算盈利金额
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close_fee = 10000 / entry * exit * 0.0005 # 平仓手续费
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logger.info(
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f"{date} {direction}({signal_name}) 入场={entry:.2f} 出场={exit:.2f} 出场时间={end_time} "
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f"差价={diff:.2f} 盈利={profit_amount:.2f} "
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f"开仓手续费=5u 平仓手续费={close_fee:.2f}"
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)
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n1 += 5 + close_fee
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n += profit_amount
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print(f'一共笔数：{len(all_trades)}')
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print(f"一共盈利：{n:.2f}")
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print(f'一共手续费：{n1:.2f}')
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28
weex/时间戳查询数据.py
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weex/时间戳查询数据.py
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import pandas as pd
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def filter_data_by_single_date(file_path, date):
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# 将传入的日期转换为 pandas 的 Timestamp 类型
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target_date = pd.Timestamp(date)
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# 计算该天的开始时间（即 00:00:00），并转换为毫秒级时间戳
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start_timestamp_ms = int(target_date.normalize().timestamp() * 1000)
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# 计算该天的结束时间（即 23:59:59.999），并转换为毫秒级时间戳
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end_timestamp_ms = int(
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(target_date.normalize() + pd.Timedelta(days=1) - pd.Timedelta(milliseconds=1)).timestamp() * 1000)
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# 读取 Excel 文件
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df = pd.read_excel(file_path)
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# 筛选出指定时间戳范围内的数据
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filtered_df = df[(df['id'] >= start_timestamp_ms) & (df['id'] <= end_timestamp_ms)]
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# 将筛选后的 DataFrame 转换为列表嵌套字典的形式
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return filtered_df.to_dict(orient='records')
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# 示例使用
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file_path = 'stock_data.xlsx'
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# 替换为你要查询的日期
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date = '2025-10-1'
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result = filter_data_by_single_date(file_path, date)
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for item in result:
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print(item)
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31
weex/读取文件分析.py
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31
weex/读取文件分析.py
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import datetime
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import pandas as pd
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from itertools import groupby
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def excel_to_list_of_dicts(file_path):
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df = pd.read_excel(file_path)
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return df.to_dict(orient='records')
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def fetch_kline(day: int, year: int = 2025, month: int = 9, period=1):
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"""获取某一天的分钟级 K线数据"""
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# 构造该日的起止时间戳
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time_ser = datetime.datetime(year, month, day) # 修正为2024年9月
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start_of_day = time_ser.replace(hour=0, minute=0, second=0, microsecond=0)
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end_of_day = time_ser.replace(hour=23, minute=59, second=59, microsecond=0)
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if __name__ == '__main__':
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file_path = 'stock_data.xlsx'
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data = excel_to_list_of_dicts(file_path)
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# 按字典内容排序
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data.sort(key=lambda x: tuple(sorted(x.items())))
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unique_data = [next(group) for _, group in groupby(data)]
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datas = sorted(unique_data, key=lambda x: x["id"])
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for i in datas:
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print(i)
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@@ -147,8 +147,8 @@ if __name__ == '__main__':
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datas.extend(sorted_data)
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sorted(datas, key=lambda x: x["id"])
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datas = sorted(datas, key=lambda x: x["id"])
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print(datas)
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daily_signals = 0 # 信号总数
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daily_wins = 0
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daily_profit = 0 # 价差总和
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@@ -165,7 +165,8 @@ if __name__ == '__main__':
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if direction and signal_type:
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daily_signals += 1
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exit_price, diff, exit_time = simulate_trade(direction, entry_open, future_candles,take_profit_diff=30, stop_loss_diff=-2)
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exit_price, diff, exit_time = simulate_trade(direction, entry_open, future_candles, take_profit_diff=30,
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stop_loss_diff=-2)
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# 统计该信号类型的表现
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signal_stats[signal_type]["count"] += 1
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169
回测数据/通过止盈止损策略.py
169
回测数据/通过止盈止损策略.py
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import datetime
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import requests
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from loguru import logger
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from typing import List, Dict, Tuple, Optional
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# ================== 常量配置 ==================
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||||
BASE_URL = "https://capi.websea.com/webApi/market/getKline"
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HEADERS = {
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'accept': 'application/json, text/plain, */*',
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||||
'origin': 'https://www.websea.com',
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||||
'referer': 'https://www.websea.com/',
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||||
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36',
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||||
}
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SYMBOL = "ETH-USDT"
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PERIOD = "15min"
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TAKE_PROFIT_DIFF = 30
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||||
STOP_LOSS_DIFF = -2
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||||
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||||
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||||
# ================== 数据获取 ==================
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def fetch_kline(day: int, year: int = 2025, month: int = 9) -> List[Dict]:
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||||
"""获取指定日期的K线数据（按时间升序返回）"""
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||||
try:
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||||
date = datetime.datetime(year, month, day)
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||||
start = int(date.replace(hour=0, minute=0, second=0).timestamp())
|
||||
end = int(date.replace(hour=23, minute=59, second=59).timestamp())
|
||||
params = {"symbol": SYMBOL, "period": PERIOD, "start": start, "end": end}
|
||||
resp = requests.get(BASE_URL, params=params, headers=HEADERS, timeout=10)
|
||||
resp.raise_for_status()
|
||||
data = resp.json().get("result", {}).get("data", [])
|
||||
return sorted(data, key=lambda x: x["id"])
|
||||
except Exception as e:
|
||||
logger.error(f"获取 {day} 号数据失败: {e}")
|
||||
return []
|
||||
|
||||
|
||||
# ================== K线辅助函数 ==================
|
||||
def is_bullish(candle: Dict) -> bool:
|
||||
return float(candle["open"]) < float(candle["close"])
|
||||
|
||||
|
||||
def is_bearish(candle: Dict) -> bool:
|
||||
return float(candle["open"]) > float(candle["close"])
|
||||
|
||||
|
||||
def check_signal(prev: Dict, curr: Dict) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""检测K线形态信号"""
|
||||
p_open, p_close = float(prev["open"]), float(prev["close"])
|
||||
c_open, c_close = float(curr["open"]), float(curr["close"])
|
||||
|
||||
if is_bullish(curr) and is_bearish(prev) and c_open < p_close and c_close > p_open:
|
||||
return "long", "bear_bull_engulf" # 涨包跌
|
||||
if is_bearish(curr) and is_bullish(prev) and c_open > p_close and c_close < p_open:
|
||||
return "short", "bull_bear_engulf" # 跌包涨
|
||||
return None, None
|
||||
|
||||
|
||||
# ================== 回测函数 ==================
|
||||
def simulate_trade(direction: str, entry_price: float, future: List[Dict],
|
||||
take_profit_diff: float, stop_loss_diff: float) -> Tuple[float, float, int]:
|
||||
"""基于未来K线模拟止盈止损"""
|
||||
for candle in future:
|
||||
high, low, close = map(float, (candle["high"], candle["low"], candle["close"]))
|
||||
cid = candle["id"]
|
||||
|
||||
if direction == "long":
|
||||
if high >= entry_price + take_profit_diff:
|
||||
return entry_price + take_profit_diff, take_profit_diff, cid
|
||||
if low <= entry_price + stop_loss_diff:
|
||||
return entry_price + stop_loss_diff, stop_loss_diff, cid
|
||||
|
||||
elif direction == "short":
|
||||
if low <= entry_price - take_profit_diff:
|
||||
return entry_price - take_profit_diff, take_profit_diff, cid
|
||||
if high >= entry_price - stop_loss_diff:
|
||||
return entry_price - stop_loss_diff, stop_loss_diff, cid
|
||||
|
||||
final_close = float(future[-1]["close"])
|
||||
diff = final_close - entry_price if direction == "long" else entry_price - final_close
|
||||
return final_close, diff, future[-1]["id"]
|
||||
|
||||
|
||||
# ================== 主程序 ==================
|
||||
def main():
|
||||
all_trades = []
|
||||
total_profit = 0.0
|
||||
|
||||
signal_stats = {
|
||||
"bear_bull_engulf": {"count": 0, "wins": 0, "profit": 0.0, "name": "涨包跌"},
|
||||
"bull_bear_engulf": {"count": 0, "wins": 0, "profit": 0.0, "name": "跌包涨"},
|
||||
}
|
||||
|
||||
for day in range(1, 31):
|
||||
data = fetch_kline(year=2025, month=9, day=day)
|
||||
if not data:
|
||||
continue
|
||||
|
||||
daily_signals = daily_wins = 0
|
||||
daily_profit = 0.0
|
||||
|
||||
for i in range(1, len(data) - 2):
|
||||
prev, curr = data[i - 1], data[i]
|
||||
entry_candle = data[i + 1]
|
||||
future_candles = data[i + 2:]
|
||||
|
||||
direction, signal_type = check_signal(prev, curr)
|
||||
if not direction:
|
||||
continue
|
||||
|
||||
entry_price = float(entry_candle["open"])
|
||||
exit_price, diff, exit_id = simulate_trade(
|
||||
direction, entry_price, future_candles, TAKE_PROFIT_DIFF, STOP_LOSS_DIFF
|
||||
)
|
||||
|
||||
daily_signals += 1
|
||||
daily_profit += diff
|
||||
signal_stats[signal_type]["count"] += 1
|
||||
signal_stats[signal_type]["profit"] += diff
|
||||
if diff > 0:
|
||||
daily_wins += 1
|
||||
signal_stats[signal_type]["wins"] += 1
|
||||
|
||||
all_trades.append({
|
||||
"date": f"{day}号",
|
||||
"entry_id": entry_candle["id"],
|
||||
"direction": "做多" if direction == "long" else "做空",
|
||||
"signal": signal_stats[signal_type]["name"],
|
||||
"entry": entry_price,
|
||||
"exit": exit_price,
|
||||
"diff": diff,
|
||||
"exit_id": exit_id,
|
||||
})
|
||||
|
||||
if daily_signals:
|
||||
win_rate = daily_wins / daily_signals * 100
|
||||
logger.info(f"{day}号: 信号={daily_signals}, 胜率={win_rate:.2f}%, 盈亏={daily_profit:.2f}")
|
||||
else:
|
||||
logger.info(f"{day}号: 无信号")
|
||||
|
||||
total_profit += daily_profit
|
||||
|
||||
logger.success(f"✅ 综合盈亏：{total_profit:.2f}")
|
||||
|
||||
# 信号统计
|
||||
logger.info("===== 信号类型统计 =====")
|
||||
for k, v in signal_stats.items():
|
||||
if v["count"]:
|
||||
wr = v["wins"] / v["count"] * 100
|
||||
logger.info(
|
||||
f"{v['name']} 信号={v['count']}, 胜率={wr:.2f}%, 总盈亏={v['profit']:.2f}, 均盈亏={v['profit'] / v['count']:.2f}")
|
||||
else:
|
||||
logger.info(f"{v['name']} 无信号")
|
||||
|
||||
# 交易明细
|
||||
total_fee = total_gain = 0.0
|
||||
logger.info("===== 每笔交易详情 =====")
|
||||
for t in all_trades:
|
||||
profit = t["diff"] / t["entry"] * 10000
|
||||
close_fee = 10000 / t["entry"] * t["exit"] * 0.0005
|
||||
total_gain += profit
|
||||
total_fee += 5 + close_fee
|
||||
logger.info(
|
||||
f"{t['date']} {t['entry_id']} {t['direction']}({t['signal']}) 入={t['entry']:.2f} 出={t['exit']:.2f} 差={t['diff']:.2f} 盈={profit:.2f} 手续费={5 + close_fee:.2f}")
|
||||
|
||||
logger.info(f"总笔数：{len(all_trades)} 总盈利：{total_gain:.2f} 总手续费：{total_fee:.2f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user