gfrdegdergr
This commit is contained in:
27942
@@ -110,6 +110,11 @@ if __name__ == '__main__':
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这样获取的，
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这样的数据，我需要解析开盘价格和结束价格，open就是开盘价格，close就是结束价格，然后id是时间戳，我需要对比，# 第一种情况：当前一笔是涨的，这里又有两种情况。第一种情况：前一笔是涨的，然后当前一笔的价格，开盘的价格小于前一笔，结盘的价格大于前一笔；第二种情况：前一笔是跌的，当前一笔的开盘价格低于前一笔的结盘价格，当前一笔的结盘价格，大于前一笔的开盘价格 ,
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这样的数据，我需要解析开盘价格和结束价格，open就是开盘价格，close就是结束价格，然后id是时间戳，我需要对比，
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# 第一种情况：当前一笔是涨的，这里又有两种情况。第一种情况：前一笔是涨的，当前一笔的价格，开盘的价格小于前一笔，结盘的价格大于前一笔；第二种情况：前一笔是跌的，当前一笔的开盘价格低于前一笔的结盘价格，当前一笔的结盘价格大于前一笔的开盘价格，
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# 第二种情况：当前一笔是跌的，这里又有两种情况。第一种情况：前一笔是跌的，当前一笔的价格，开盘的价格大于前一笔，结盘的价格小于前一笔；第二种情况，前一笔是涨的，当前一笔的开盘价格大于前一笔的结盘价格，当前价格的结盘价格小于前一笔的开盘价格，
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这样就是当前价格从k线图中看，就是前一笔的开盘价和结盘价处于当前一笔的中间包裹住的情况，然后需要查询出来
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然后我需要预测出胜率，当前一笔是涨的，两笔后的结算价格大于当前一笔的结算价格，就是赚，
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当前一笔是跌的，两笔后的结算价格小于当前一笔的结算价格，就是赚，
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这样就是当前价格从k线图中看，就是前一笔的开盘价和结盘价处于当前一笔的开盘价和结盘价中间包裹住的情况，然后需要查询出来
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58
回测数据/接口分析.py
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58
回测数据/接口分析.py
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import datetime
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import requests
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headers = {
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'accept': 'application/json, text/plain, */*',
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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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if __name__ == '__main__':
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# 指定日期
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time_ser = datetime.datetime(2025, 9, 25)
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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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start_timestamp = int(start_of_day.timestamp())
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end_timestamp = int(end_of_day.timestamp())
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params = {
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'symbol': 'ETH-USDT',
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'period': '1min',
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'start': start_timestamp,
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'end': end_timestamp,
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}
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response = requests.get('https://capi.websea.com/webApi/market/getKline',
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params=params, headers=headers)
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data = response.json()['result']['data']
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# 排序
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sorted_data = sorted(data, key=lambda x: x['id'])
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for idx in range(1, len(sorted_data)):
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prev = sorted_data[idx - 1] # 前一笔
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curr = sorted_data[idx] # 当前一笔
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po, pc = float(prev['open']), float(prev['close'])
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co, cc = float(curr['open']), float(curr['close'])
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prev_up = pc > po # 前一笔涨
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curr_up = cc > co # 当前一笔涨
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# -------- 第一种情况：当前是涨 --------
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if curr_up:
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# ① 前一笔是涨的
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if prev_up and (co < po and cc > pc):
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print(f"涨-包裹(前涨,当前涨) id={curr['id']} 前={po,pc} 当前={co,cc}")
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# ② 前一笔是跌的
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if not prev_up and (co < pc and cc > po):
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print(f"涨-包裹(前跌,当前涨) id={curr['id']} 前={po,pc} 当前={co,cc}")
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# -------- 第二种情况：当前是跌 --------
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else:
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# ① 前一笔是跌的
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if not prev_up and (co > po and cc < pc):
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print(f"跌-包裹(前跌,当前跌) id={curr['id']} 前={po,pc} 当前={co,cc}")
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# ② 前一笔是涨的
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if prev_up and (co > pc and cc < po):
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print(f"跌-包裹(前涨,当前跌) id={curr['id']} 前={po,pc} 当前={co,cc}")
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80
回测数据/接口分析，回测胜率.py
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80
回测数据/接口分析，回测胜率.py
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import datetime
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import requests
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import pandas as pd
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headers = {
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'accept': 'application/json, text/plain, */*',
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'accept-language': 'zh,zh-CN;q=0.9,zh-HK;q=0.8,en;q=0.7',
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'cache-control': 'no-cache',
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'origin': 'https://www.websea.com',
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'pragma': 'no-cache',
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'priority': 'u=1, i',
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'referer': 'https://www.websea.com/',
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'sec-ch-ua': '"Chromium";v="140", "Not=A?Brand";v="24", "Google Chrome";v="140"',
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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': 'same-site',
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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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if __name__ == '__main__':
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# 请求数据
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time_ser = datetime.datetime(2025, 9, 24)
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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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params = {
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'symbol': 'ETH-USDT',
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'period': '1min',
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'start': int(start_of_day.timestamp()),
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'end': int(end_of_day.timestamp()),
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}
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response = requests.get('https://capi.websea.com/webApi/market/getKline', params=params, headers=headers)
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data = response.json()['result']['data']
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sorted_data = sorted(data, key=lambda x: x['id'])
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signals = 0
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wins = 0
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for idx in range(1, len(sorted_data) - 2): # 需要至少留两根K线做验证
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prev = sorted_data[idx - 1] # 前一笔
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curr = sorted_data[idx] # 当前这笔
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future = sorted_data[idx + 2] # 两根后的K线
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prev_open, prev_close = float(prev['open']), float(prev['close'])
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curr_open, curr_close = float(curr['open']), float(curr['close'])
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future_close = float(future['close'])
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# 当前为涨
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if curr_close > curr_open:
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# 前一笔涨 + 包裹
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if prev_close > prev_open and curr_open < prev_open and curr_close > prev_close:
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signals += 1
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if future_close > curr_close:
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wins += 1
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# 前一笔跌 + 反包
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elif prev_close < prev_open and curr_open < prev_close and curr_close > prev_open:
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signals += 1
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if future_close > curr_close:
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wins += 1
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# 当前为跌
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elif curr_close < curr_open:
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# 前一笔跌 + 包裹
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if prev_close < prev_open and curr_open > prev_open and curr_close < prev_close:
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signals += 1
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if future_close < curr_close:
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wins += 1
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# 前一笔涨 + 反包
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elif prev_close > prev_open and curr_open > prev_close and curr_close < prev_open:
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signals += 1
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if future_close < curr_close:
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wins += 1
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if signals > 0:
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win_rate = wins / signals * 100
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print(f"信号数={signals}, 胜率={win_rate:.2f}%")
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else:
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print("没有找到符合条件的形态")
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120
回测数据/接口分析，绘制k线.py
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120
回测数据/接口分析，绘制k线.py
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import datetime
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import requests
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import pandas as pd
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import plotly.graph_objects as go
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headers = {
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'accept': 'application/json, text/plain, */*',
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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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def fetch_kline():
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time_ser = datetime.datetime(2025, 9, 26)
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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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start_timestamp = int(start_of_day.timestamp())
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end_timestamp = int(end_of_day.timestamp())
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params = {
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'symbol': 'ETH-USDT',
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'period': '1min',
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'start': start_timestamp,
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'end': end_timestamp,
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}
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response = requests.get('https://capi.websea.com/webApi/market/getKline',
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params=params, headers=headers)
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data = response.json()['result']['data']
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sorted_data = sorted(data, key=lambda x: x['id'])
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df = pd.DataFrame(sorted_data)
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df['Date'] = pd.to_datetime(df['id'], unit='s')
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df.set_index('Date', inplace=True)
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df.rename(columns={
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'open': 'Open',
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'close': 'Close',
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'high': 'High',
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'low': 'Low',
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'amount': 'Volume'
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}, inplace=True)
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df[['Open', 'Close', 'High', 'Low', 'Volume']] = df[['Open', 'Close', 'High', 'Low', 'Volume']].astype(float)
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return df
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def detect_signals(df):
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signals = []
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for idx in range(1, len(df)):
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prev = df.iloc[idx - 1]
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curr = df.iloc[idx]
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po, pc = prev['Open'], prev['Close']
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co, cc = curr['Open'], curr['Close']
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prev_up = pc > po
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curr_up = cc > co
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if curr_up:
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if prev_up and (co < po and cc > pc):
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signals.append((curr.name, 'bull'))
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if not prev_up and (co < pc and cc > po):
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signals.append((curr.name, 'bull'))
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else:
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if not prev_up and (co > po and cc < pc):
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signals.append((curr.name, 'bear'))
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if prev_up and (co > pc and cc < po):
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signals.append((curr.name, 'bear'))
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return signals
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def plot_signals(df, signals):
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fig = go.Figure()
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# 蜡烛图
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fig.add_trace(go.Candlestick(
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x=df.index,
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open=df['Open'],
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high=df['High'],
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low=df['Low'],
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close=df['Close'],
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name='K线',
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increasing_line_color='green',
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decreasing_line_color='red'
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))
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# 标记信号
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bull_x = [dt for dt, sig in signals if sig == 'bull']
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bull_y = [df.loc[dt, 'Close'] for dt, sig in signals if sig == 'bull']
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bear_x = [dt for dt, sig in signals if sig == 'bear']
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bear_y = [df.loc[dt, 'Close'] for dt, sig in signals if sig == 'bear']
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fig.add_trace(go.Scatter(
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x=bull_x, y=bull_y,
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mode="markers",
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marker=dict(symbol="triangle-up", size=12, color="lime"),
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name="看涨信号"
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))
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fig.add_trace(go.Scatter(
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x=bear_x, y=bear_y,
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mode="markers",
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marker=dict(symbol="triangle-down", size=12, color="orange"),
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name="看跌信号"
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))
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# 美化布局
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fig.update_layout(
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title="ETH-USDT 包裹信号",
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xaxis_title="时间",
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yaxis_title="价格 (USDT)",
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template="plotly_dark", # 黑色主题，比较炫酷
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xaxis_rangeslider_visible=False, # 隐藏默认范围滑块
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hovermode="x unified" # 鼠标悬浮时统一显示数据
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)
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fig.show()
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if __name__ == "__main__":
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df = fetch_kline()
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signals = detect_signals(df)
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plot_signals(df, signals)
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