UbuntuandCursor
206b27fe72
chanlun/indicators/ta.py 接口兼容 talib.abstract，实现代码实际用到的 SMA/MA/EMA/RSI/ATR/MACD/BBANDS；chanlun/pipeline/resample.py 替代 technical.util.resample_to_interval。调用点只改 import，逻辑未动。 暖机长度与平滑种子按 TA-Lib 的约定实现，差一根 K 线就会让下游所有 笔/线段/中枢整体位移。其中 MACD 需特别处理：TA-Lib 让快慢两条 EMA 在同一根 K 线出首值，因而快线的种子取 x[slow-fast:slow] 的均值，而非 从 fastperiod-1 一路递推——两者在百元价位上相差约 0.17。 BBANDS 是有意的分歧：TA-Lib 用 sumsq/n - mean² 求方差，短窗口远离零 时灾难性抵消（timeperiod=2 误差 8.7e-7），本实现用 rolling std，对 50 位精度基准误差为 0。项目实际使用的周期两者一致到 1e-10。 顺带清理 12 个文件中 16 处从未调用的 talib/technical 导入。 验证：9440 组随机对拨；真实 K 线端到端比对 add_indicators 全部 33 个 指标列，NaN 模式一致、MACD 柱符号 100% 相同；屏蔽两个包后 60 个模块 均可导入。新增 test_ta_compat.py 将输出逐 bar 钉在 TA-Lib 上，但该文件 在 TA-Lib 缺失时静默跳过，改动 ta.py 需在装有 TA-Lib 的环境复跑。 Co-authored-by: Cursor <cursoragent@cursor.com>
449 lines
18 KiB
Python
449 lines
18 KiB
Python
import ccxt
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import pandas as pd
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import numpy as np
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import mplfinance as mpf
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from chanlun.indicators import ta
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from datetime import datetime, timedelta
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import logging
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import datetime as dt
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# Configure logging
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logging.basicConfig(
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filename='chanlun_trading.log',
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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# Configuration (user to modify)
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BINANCE_API_KEY = 'your_api_key' # Replace with your Binance API key
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BINANCE_API_SECRET = 'your_api_secret' # Replace with your Binance API secret
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SIMULATION_MODE = True # Set to False for live trading
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# 1. Fetch K-line data from Binance (multi-timeframe support)
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def fetch_binance_data(symbol='BTC/USDT', timeframe='5m', limit=500):
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try:
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exchange = ccxt.binance({
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'apiKey': BINANCE_API_KEY if not SIMULATION_MODE else '',
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'secret': BINANCE_API_SECRET if not SIMULATION_MODE else '',
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'enableRateLimit': True,
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'options': {'defaultType': 'spot'}
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})
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since = exchange.parse8601((datetime.now(dt.UTC) - timedelta(days=7)).isoformat())
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ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since, limit)
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df = pd.DataFrame(ohlcv, columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])
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df['Date'] = pd.to_datetime(df['Date'], unit='ms')
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df.set_index('Date', inplace=True)
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logging.info(f"Fetched {len(df)} K-lines for {symbol} ({timeframe})")
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return df
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except Exception as e:
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logging.error(f"Failed to fetch data: {e}")
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raise
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# 2. K-line merging (vectorized)
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def merge_kline(df):
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try:
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df = df.copy()
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merged_data = []
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trend = np.sign(df['Close'].diff().shift(-1)) # 1: up, -1: down, 0: neutral
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# Detect inclusion
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is_included = ((df['High'].shift(-1) <= df['High']) & (df['Low'].shift(-1) >= df['Low'])) | \
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((df['High'].shift(-1) >= df['High']) & (df['Low'].shift(-1) <= df['Low']))
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i = 0
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while i < len(df) - 1:
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if is_included.iloc[i]:
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current_k = df.iloc[i]
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next_k = df.iloc[i + 1]
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high = max(current_k['High'], next_k['High'])
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low = min(current_k['Low'], next_k['Low'])
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open_price = current_k['Open']
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close_price = next_k['Close'] if trend.iloc[i] >= 0 else next_k['Close']
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volume = current_k['Volume'] + next_k['Volume']
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merged_data.append({
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'Date': next_k.name,
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'Open': open_price,
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'High': high,
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'Low': low,
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'Close': close_price,
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'Volume': volume
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})
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i += 2
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else:
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current_k = df.iloc[i]
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merged_data.append({
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'Date': current_k.name,
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'Open': current_k['Open'],
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'High': current_k['High'],
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'Low': current_k['Low'],
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'Close': current_k['Close'],
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'Volume': current_k['Volume']
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})
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i += 1
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if i == len(df) - 1:
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last_k = df.iloc[i]
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merged_data.append({
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'Date': last_k.name,
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'Open': last_k['Open'],
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'High': last_k['High'],
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'Low': last_k['Low'],
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'Close': last_k['Close'],
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'Volume': last_k['Volume']
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})
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merged_df = pd.DataFrame(merged_data)
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merged_df['Date'] = pd.to_datetime(merged_df['Date'])
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merged_df.set_index('Date', inplace=True)
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logging.info(f"Merged K-lines: {len(df)} -> {len(merged_df)}")
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return merged_df
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except Exception as e:
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logging.error(f"K-line merging failed: {e}")
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raise
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# 3. Detect fractals (vectorized)
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def detect_fractals(df):
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try:
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df = df.copy()
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df['is_top'] = (df['High'] > df['High'].shift(1)) & (df['High'] > df['High'].shift(-1)) & \
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(df['High'] > df['High'].shift(2)) & (df['High'] > df['High'].shift(-2))
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df['is_bottom'] = (df['Low'] < df['Low'].shift(1)) & (df['Low'] < df['Low'].shift(-1)) & \
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(df['Low'] < df['Low'].shift(2)) & (df['Low'] < df['Low'].shift(-2))
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df['is_top'] = df['is_top'].fillna(False)
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df['is_bottom'] = df['is_bottom'].fillna(False)
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logging.info(f"Detected {df['is_top'].sum()} top fractals and {df['is_bottom'].sum()} bottom fractals")
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return df
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except Exception as e:
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logging.error(f"Fractal detection failed: {e}")
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raise
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# 4. Detect strokes
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def detect_strokes(df):
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try:
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strokes = []
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last_fractal = None
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last_price = None
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last_index = None
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for i in range(len(df)):
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if df['is_top'].iloc[i] or df['is_bottom'].iloc[i]:
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current_fractal = 'top' if df['is_top'].iloc[i] else 'bottom'
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current_price = df['High'].iloc[i] if current_fractal == 'top' else df['Low'].iloc[i]
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if last_fractal is None:
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last_fractal = current_fractal
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last_price = current_price
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last_index = df.index[i]
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continue
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if (last_fractal == 'top' and current_fractal == 'bottom' and current_price < last_price) or \
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(last_fractal == 'bottom' and current_fractal == 'top' and current_price > last_price):
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strokes.append({
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'start_time': last_index,
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'end_time': df.index[i],
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'start_price': last_price,
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'end_price': current_price,
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'type': 'down' if current_fractal == 'bottom' else 'up',
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'volume': df['Volume'].loc[last_index:df.index[i]].sum()
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})
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last_fractal = current_fractal
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last_price = current_price
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last_index = df.index[i]
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logging.info(f"Detected {len(strokes)} strokes")
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return strokes
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except Exception as e:
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logging.error(f"Stroke detection failed: {e}")
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raise
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# 5. Detect segments
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def detect_segments(strokes):
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try:
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segments = []
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if len(strokes) < 3:
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return segments
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i = 0
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while i < len(strokes) - 2:
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stroke1, stroke2, stroke3 = strokes[i], strokes[i+1], strokes[i+2]
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if stroke1['type'] == 'up' and stroke2['type'] == 'down' and stroke3['type'] == 'up':
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if stroke3['end_price'] > stroke1['end_price']:
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segments.append({
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'start_time': stroke1['start_time'],
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'end_time': stroke3['end_time'],
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'start_price': stroke1['start_price'],
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'end_price': stroke3['end_price'],
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'type': 'up'
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})
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i += 3
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else:
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i += 1
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elif stroke1['type'] == 'down' and stroke2['type'] == 'up' and stroke3['type'] == 'down':
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if stroke3['end_price'] < stroke1['end_price']:
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segments.append({
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'start_time': stroke1['start_time'],
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'end_time': stroke3['end_time'],
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'start_price': stroke1['start_price'],
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'end_price': stroke3['end_price'],
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'type': 'down'
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})
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i += 3
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else:
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i += 1
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else:
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i += 1
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logging.info(f"Detected {len(segments)} segments")
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return segments
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except Exception as e:
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logging.error(f"Segment detection failed: {e}")
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raise
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# 6. Detect pivots (midlines)
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def detect_pivots(strokes):
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try:
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pivots = []
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if len(strokes) < 3:
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return pivots
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for i in range(len(strokes) - 2):
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s1, s2, s3 = strokes[i:i+3]
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high = min(s1['start_price'], s1['end_price'], s2['start_price'], s2['end_price'],
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s3['start_price'], s3['end_price'])
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low = max(s1['start_price'], s1['end_price'], s2['start_price'], s2['end_price'],
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s3['start_price'], s3['end_price'])
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if high > low:
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pivots.append({
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'start_time': s1['start_time'],
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'end_time': s3['end_time'],
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'high': high,
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'low': low
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})
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logging.info(f"Detected {len(pivots)} pivots")
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return pivots
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except Exception as e:
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logging.error(f"Pivot detection failed: {e}")
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raise
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# 7. Analyze higher timeframe (30m)
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def analyze_higher_timeframe(df_30m):
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try:
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df_30m = detect_fractals(df_30m)
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strokes_30m = detect_strokes(df_30m)
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if not strokes_30m:
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return 'neutral'
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last_stroke = strokes_30m[-1]
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logging.info(f"30m trend: {last_stroke['type']}")
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return last_stroke['type']
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except Exception as e:
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logging.error(f"Higher timeframe analysis failed: {e}")
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raise
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# 8. Back-divergence detection (enhanced)
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def detect_back_divergence(df, strokes, higher_trend):
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try:
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macd_df = ta.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
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macd, hist = macd_df['macd'], macd_df['macdhist']
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sma20 = ta.SMA(df['Close'], timeperiod=20)
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df['macd'] = macd
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df['hist'] = hist
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df['sma20'] = sma20
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df['buy_signal'] = False
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df['sell_signal'] = False
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stroke_metrics = []
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for stroke in strokes:
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start_idx = df.index.get_loc(stroke['start_time'])
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end_idx = df.index.get_loc(stroke['end_time'])
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hist_segment = df['hist'].iloc[start_idx:end_idx+1]
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price_change = abs(stroke['end_price'] - stroke['start_price'])
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hist_area = sum(abs(h) for h in hist_segment if not np.isnan(h))
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volume = stroke['volume']
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stroke_metrics.append({
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'start_time': stroke['start_time'],
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'end_time': stroke['end_time'],
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'type': stroke['type'],
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'price_change': price_change,
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'hist_area': hist_area,
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'volume': volume
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})
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for i in range(2, len(stroke_metrics)):
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current_stroke = stroke_metrics[i]
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prev_stroke = stroke_metrics[i-2]
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if current_stroke['type'] != prev_stroke['type']:
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continue
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current_end_idx = df.index.get_loc(current_stroke['end_time'])
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# Uptrend back-divergence (sell signal)
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if current_stroke['type'] == 'up':
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price_increase = df['High'].loc[current_stroke['end_time']] > df['High'].loc[prev_stroke['end_time']]
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hist_decrease = current_stroke['hist_area'] < prev_stroke['hist_area']
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volume_decrease = current_stroke['volume'] < prev_stroke['volume']
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is_top_fractal = df['is_top'].loc[current_stroke['end_time']]
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hist_positive = df['hist'].iloc[current_end_idx] > 0 or \
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(df['hist'].iloc[current_end_idx] < 0 and df['hist'].iloc[current_end_idx-1] > 0)
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sma_trend = df['Close'].iloc[current_end_idx] > df['sma20'].iloc[current_end_idx]
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trend_match = higher_trend in ['up', 'neutral']
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if price_increase and hist_decrease and volume_decrease and is_top_fractal and \
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hist_positive and sma_trend and trend_match:
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df.loc[df.index[current_end_idx], 'sell_signal'] = True
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# Downtrend back-divergence (buy signal)
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elif current_stroke['type'] == 'down':
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price_decrease = df['Low'].loc[current_stroke['end_time']] < df['Low'].loc[prev_stroke['end_time']]
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hist_decrease = current_stroke['hist_area'] < prev_stroke['hist_area']
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volume_decrease = current_stroke['volume'] < prev_stroke['volume']
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is_bottom_fractal = df['is_bottom'].loc[current_stroke['end_time']]
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hist_negative = df['hist'].iloc[current_end_idx] < 0 or \
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(df['hist'].iloc[current_end_idx] > 0 and df['hist'].iloc[current_end_idx-1] < 0)
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sma_trend = df['Close'].iloc[current_end_idx] < df['sma20'].iloc[current_end_idx]
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trend_match = higher_trend in ['down', 'neutral']
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if price_decrease and hist_decrease and volume_decrease and is_bottom_fractal and \
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hist_negative and sma_trend and trend_match:
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df.loc[df.index[current_end_idx], 'buy_signal'] = True
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logging.info(f"Detected {df['buy_signal'].sum()} buy signals and {df['sell_signal'].sum()} sell signals")
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return df
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except Exception as e:
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logging.error(f"Back-divergence detection failed: {e}")
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raise
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# 9. Execute trade
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def execute_trade(exchange, symbol, signal, amount=0.001):
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try:
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if SIMULATION_MODE:
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msg = f"[SIMULATION] {'Buy' if signal == 'buy' else 'Sell'} {amount} {symbol} at {datetime.now(dt.UTC)}"
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print(msg)
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logging.info(msg)
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return
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if signal == 'buy':
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order = exchange.create_market_buy_order(symbol, amount)
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msg = f"Buy order executed: {order}"
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print(msg)
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logging.info(msg)
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elif signal == 'sell':
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order = exchange.create_market_sell_order(symbol, amount)
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msg = f"Sell order executed: {order}"
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print(msg)
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logging.info(msg)
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except Exception as e:
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msg = f"Trade execution failed: {e}"
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print(msg)
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logging.error(msg)
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# 10. Plot chart
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def plot_chart(df, strokes, segments, pivots):
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try:
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# Initialize additional plots
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apds = []
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alines = [] # For line segments
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# Plot strokes as line segments
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for stroke in strokes:
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alines.append([(stroke['start_time'], stroke['start_price']),
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(stroke['end_time'], stroke['end_price'])])
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# Plot segments as line segments
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for segment in segments:
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alines.append([(segment['start_time'], segment['start_price']),
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(segment['end_time'], segment['end_price'])])
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# Plot pivots as horizontal lines
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for pivot in pivots:
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alines.append([(pivot['start_time'], pivot['high']),
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(pivot['end_time'], pivot['high'])])
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alines.append([(pivot['start_time'], pivot['low']),
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(pivot['end_time'], pivot['low'])])
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# Add alines to plot (single color for simplicity, can customize)
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if alines:
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apds.append(mpf.make_addplot(
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None, # No y-data needed for alines
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alines=alines,
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type='line',
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color=['blue' if i < len(strokes) else 'purple' if i < len(strokes) + len(segments) else 'orange'
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for i in range(len(alines))],
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linestyle=['--' if i < len(strokes) else '-' if i < len(strokes) + len(segments) else ':'
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for i in range(len(alines))]
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))
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# Plot buy/sell signals
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buy_signals = df[df['buy_signal']]['Close']
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sell_signals = df[df['sell_signal']]['Close']
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apds.append(mpf.make_addplot(buy_signals, type='scatter', markersize=100, marker='^', color='green'))
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apds.append(mpf.make_addplot(sell_signals, type='scatter', markersize=100, marker='v', color='red'))
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# Plot K-line chart
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mpf.plot(df, type='candle', addplot=apds, title='Chanlun Advanced Analysis', style='yahoo')
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logging.info("Chart plotted successfully")
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except Exception as e:
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logging.error(f"Chart plotting failed: {e}")
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raise
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# 11. Main function
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def main():
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try:
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# Initialize exchange
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exchange = ccxt.binance({
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'apiKey': BINANCE_API_KEY if not SIMULATION_MODE else '',
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'secret': BINANCE_API_SECRET if not SIMULATION_MODE else '',
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'enableRateLimit': True,
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'options': {'defaultType': 'spot'}
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})
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# Fetch data
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df_5m = fetch_binance_data(symbol='BTC/USDT', timeframe='5m', limit=500)
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df_30m = fetch_binance_data(symbol='BTC/USDT', timeframe='30m', limit=200)
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# Merge 5m K-lines
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df_5m = merge_kline(df_5m)
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# Detect fractals, strokes, segments, pivots
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df_5m = detect_fractals(df_5m)
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strokes = detect_strokes(df_5m)
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segments = detect_segments(strokes)
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pivots = detect_pivots(strokes)
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|
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# Analyze 30m trend
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higher_trend = analyze_higher_timeframe(df_30m)
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print(f"30m Trend: {higher_trend}")
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|
|
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# Detect back-divergence
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df_5m = detect_back_divergence(df_5m, strokes, higher_trend)
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|
|
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# Plot chart
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|
plot_chart(df_5m, strokes, segments, pivots)
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|
|
|
# Output and execute trades
|
|
print("Buy Signals:")
|
|
buy_signals = df_5m[df_5m['buy_signal']][['Close']]
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|
print(buy_signals)
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|
for idx, row in buy_signals.iterrows():
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execute_trade(exchange, 'BTC/USDT', 'buy', amount=0.001)
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|
|
|
print("Sell Signals:")
|
|
sell_signals = df_5m[df_5m['sell_signal']][['Close']]
|
|
print(sell_signals)
|
|
for idx, row in sell_signals.iterrows():
|
|
execute_trade(exchange, 'BTC/USDT', 'sell', amount=0.001)
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|
|
|
logging.info("Main function completed successfully")
|
|
except Exception as e:
|
|
logging.error(f"Main function failed: {e}")
|
|
raise
|
|
|
|
if __name__ == "__main__":
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|
main() |