添加k线动能理论

web/app.py

@@ -267,6 +267,18 @@ def add_indicators(df):
     df['ma10'] = (ta.MA(df, timeperiod=10)).fillna(0)
     df['ma30'] = (ta.EMA(df, timeperiod=30)).fillna(0)
     df['ma250'] = (ta.MA(df, timeperiod=250)).fillna(0)
+    # 新增 EMA 指标
+    df['ema5'] = (ta.EMA(df, timeperiod=5)).fillna(0)
+    df['ema10'] = (ta.EMA(df, timeperiod=10)).fillna(0)
+    df['ema24'] = (ta.EMA(df, timeperiod=24)).fillna(0)
+    df['ema52'] = (ta.EMA(df, timeperiod=52)).fillna(0)
+    # 常用SMA 24/52
+    try:
+        df['sma24'] = (ta.SMA(df, timeperiod=24)).fillna(0)
+        df['sma52'] = (ta.SMA(df, timeperiod=52)).fillna(0)
+    except Exception:
+        df['sma24'] = 0
+        df['sma52'] = 0
     df['rsi'] = ta.RSI(df, timeperiod=14)
     
     # 计算布林带 (当前周期 - 20周期，2标准差)
@@ -275,9 +287,11 @@ def add_indicators(df):
     df['bb_middle'] = bb['middleband'].fillna(0)
     df['bb_lower'] = bb['lowerband'].fillna(0)
     bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
+    bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
     df['bbup30'] = bb30['upperband'].fillna(0)
     df['bblow30'] = bb30['lowerband'].fillna(0)
     bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
+    bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
     df['bbup302'] = bb302['upperband'].fillna(0)
     df['bblow302'] = bb302['lowerband'].fillna(0)
     # 计算次周期布林带 (14周期，2标准差)
@@ -293,6 +307,12 @@ def add_indicators(df):
     df['ma10'] = df['ma10'].fillna(0)
     df['ma30'] = df['ma30'].fillna(0)
     df['ma250'] = df['ma250'].fillna(0)
+    df['ema5'] = df['ema5'].fillna(0)
+    df['ema10'] = df['ema10'].fillna(0)
+    df['ema24'] = df['ema24'].fillna(0)
+    df['ema52'] = df['ema52'].fillna(0)
+    df['sma24'] = df['sma24'].fillna(0)
+    df['sma52'] = df['sma52'].fillna(0)
     df['rsi'] = df['rsi'].fillna(0)
     df['avg_volume'] = df['volume'].rolling(10).mean()
     # 计算量比，避免产生Infinity值
@@ -312,10 +332,10 @@ def add_indicators(df):
 
 def calculate_macd(df):
     """计算MACD指标"""
-    exp1 = df['close'].ewm(span=24, adjust=False).mean()
-    exp2 = df['close'].ewm(span=52, adjust=False).mean()
+    exp1 = df['close'].ewm(span=12, adjust=False).mean()
+    exp2 = df['close'].ewm(span=26, adjust=False).mean()
     macd = exp1 - exp2
-    signal = macd.ewm(span=18, adjust=False).mean()
+    signal = macd.ewm(span=9, adjust=False).mean()
     histogram = macd - signal
     
     return {
@@ -1035,6 +1055,228 @@ def clean_dataframe_for_json(df):
     
     return clean_df
 
+# ====== 趋势判定与趋势筛选（币对） ======
+
+def classify_trend_stage(df):
+    """根据 EMA 斜率与多空排列判断趋势方向与阶段
+    返回: direction in {"bull","bear","sideways"}, stage in {"early","mid","late"}, strength_score (0-100)
+    """
+    if df is None or len(df) < 60:
+        return "sideways", "early", 0
+
+    # 使用 EMA5/10/24/52
+    closes = df['close'].values
+    ema5 = df['ema5'].values if 'ema5' in df else ta.EMA(df, timeperiod=5)
+    ema10 = df['ema10'].values if 'ema10' in df else ta.EMA(df, timeperiod=10)
+    ema24 = df['ema24'].values if 'ema24' in df else ta.EMA(df, timeperiod=24)
+    ema52 = df['ema52'].values if 'ema52' in df else ta.EMA(df, timeperiod=52)
+
+    # 最近N根用于斜率与排列判定
+    lookback = min(30, len(df) - 1)
+    if lookback <= 5:
+        return "sideways", "early", 0
+
+    # 简单斜率: 最近k根的线性变化率近似
+    def slope(arr, k=10):
+        k = min(k, len(arr) - 1)
+        if k < 2:
+            return 0.0
+        y = arr[-k:]
+        x = np.arange(k)
+        # 最小二乘拟合斜率
+        denom = np.dot(x - x.mean(), x - x.mean())
+        if denom == 0:
+            return 0.0
+        m = np.dot(y - y.mean(), x - x.mean()) / denom
+        return float(m)
+
+    k_slope = 12  # 斜率窗口
+    s5 = slope(ema5, k_slope)
+    s10 = slope(ema10, k_slope)
+    s24 = slope(ema24, k_slope)
+    s52 = slope(ema52, k_slope)
+
+    # 多空排列
+    last5, last10, last24, last52 = ema5[-1], ema10[-1], ema24[-1], ema52[-1]
+    bull_stack = last5 > last10 > last24 > last52
+    bear_stack = last5 < last10 < last24 < last52
+
+    # 波动性与动量增强: MACD 柱体最近均值
+    macdhist = df['macdhist'].values if 'macdhist' in df else calculate_macd(df)['histogram']
+    hist_recent = macdhist[-lookback:]
+    hist_power = float(np.mean(np.abs(hist_recent))) if len(hist_recent) else 0.0
+
+    # 方向
+    if bull_stack and s24 > 0 and s52 > 0:
+        direction = "bull"
+    elif bear_stack and s24 < 0 and s52 < 0:
+        direction = "bear"
+    else:
+        # 用价格相对 EMA52 辅助
+        if closes[-1] > last52 and (s24 + s52) > 0:
+            direction = "bull"
+        elif closes[-1] < last52 and (s24 + s52) < 0:
+            direction = "bear"
+        else:
+            direction = "sideways"
+
+    # 阶段: 依据（斜率大小、与EMA52距离、MACD柱体扩张/收敛）
+    dist52 = float((closes[-1] - last52) / last52) if last52 else 0.0
+    slope_score = max(0.0, (abs(s24) + abs(s52)) * 1000.0)  # 归一化
+    dist_score = min(50.0, abs(dist52) * 200.0)
+    hist_score = min(30.0, hist_power * 10.0)
+    strength = float(min(100.0, slope_score + dist_score + hist_score))
+
+    # 简单阶段判定
+    if direction == "sideways":
+        stage = "early"
+        strength = min(strength, 30.0)
+    else:
+        # 查看最近 hist 是否在扩大或收敛
+        if len(hist_recent) >= 6:
+            recent_growth = np.mean(np.abs(hist_recent[-3:])) - np.mean(np.abs(hist_recent[-6:-3]))
+        else:
+            recent_growth = 0.0
+
+        if recent_growth > 0 and abs(dist52) < 0.05:
+            stage = "early"
+        elif recent_growth > 0 and abs(dist52) >= 0.05:
+            stage = "mid"
+        else:
+            stage = "late"
+
+    return direction, stage, strength
+
+
+def load_crypto_symbols(limit=200):
+    """加载常见USDT永续合约交易对，返回列表"""
+    try:
+        markets = exchange.load_markets()
+        symbols = [s for s in markets.keys() if '/USDT' in s and ':USDT' in s]
+        return symbols[:limit]
+    except Exception:
+        return SYMBOLS
+
+
+@app.route('/api/trend_filter', methods=['GET'])
+def trend_filter():
+    """趋势筛选接口（币对）
+    参数:
+      timeframe: K线周期
+      start_time, end_time: 毫秒时间戳，可选
+      direction: bull/bear/sideways 可选
+      stage: early/mid/late 可选
+      min_strength: 0-100 可选
+      symbols: 逗号分隔列表，可选；不传则自动加载部分USDT币对
+    返回符合条件的币对与简要统计
+    """
+    timeframe = request.args.get('timeframe', '1h')
+    start_time = request.args.get('start_time')
+    end_time = request.args.get('end_time')
+    want_direction = request.args.get('direction')  # 可为 None
+    want_stage = request.args.get('stage')  # 可为 None
+    try:
+        min_strength = float(request.args.get('min_strength', '0'))
+    except ValueError:
+        min_strength = 0.0
+
+    symbols_param = request.args.get('symbols')
+    if symbols_param:
+        symbols_list = [s.strip() for s in symbols_param.split(',') if s.strip()]
+    else:
+        symbols_list = load_crypto_symbols(limit=150)
+
+    results = []
+    for sym in symbols_list:
+        try:
+            df = get_crypto_kl_data(sym, timeframe, start_time=start_time, end_time=end_time)
+            if df is None or len(df) < 60:
+                continue
+            df = add_indicators(df)
+            direction, stage, strength = classify_trend_stage(df)
+
+            if want_direction and direction != want_direction:
+                continue
+            if want_stage and stage != want_stage:
+                continue
+            if strength < min_strength:
+                continue
+
+            last_row = df.iloc[-1]
+            results.append({
+                'symbol': sym,
+                'time': int(last_row['timestamp']),
+                'close': float(last_row['close']),
+                'direction': direction,
+                'stage': stage,
+                'strength': float(round(strength, 2)),
+                'ema5': float(last_row['ema5']),
+                'ema10': float(last_row['ema10']),
+                'ema24': float(last_row['ema24']),
+                'ema52': float(last_row['ema52'])
+            })
+        except Exception:
+            continue
+
+    # 按强度降序
+    results.sort(key=lambda x: x['strength'], reverse=True)
+    return jsonify({
+        'count': len(results),
+        'results': results
+    })
+
+
+@app.route('/api/trend_detail', methods=['GET'])
+def trend_detail():
+    """返回单个币对的K线与EMA、用于前端绘制趋势线
+    参数: symbol, timeframe, start_time, end_time
+    """
+    symbol = request.args.get('symbol')
+    timeframe = request.args.get('timeframe', '1h')
+    start_time = request.args.get('start_time')
+    end_time = request.args.get('end_time')
+    timezone_name = request.args.get('timezone', 'Asia/Shanghai')
+
+    if not symbol:
+        return jsonify({'error': 'symbol不能为空'})
+
+    df = get_crypto_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
+    if df is None or len(df) == 0:
+        return jsonify({'error': '获取数据失败'})
+
+    df = add_indicators(df)
+    direction, stage, strength = classify_trend_stage(df)
+
+    # 简单趋势线: 用最近N根收盘价做线性拟合
+    N = min(80, len(df))
+    sub = df.tail(N)
+    y = sub['close'].values
+    x = np.arange(len(y))
+    denom = np.dot(x - x.mean(), x - x.mean())
+    if denom != 0:
+        m = float(np.dot(y - y.mean(), x - x.mean()) / denom)
+        b = float(y.mean() - m * x.mean())
+    else:
+        m, b = 0.0, float(y[-1])
+
+    client_tz = timezone(timezone_name)
+
+    return jsonify({
+        'symbol': symbol,
+        'timeframe': timeframe,
+        'timezone': timezone_name,
+        'direction': direction,
+        'stage': stage,
+        'strength': float(round(strength, 2)),
+        'kline_data': clean_dataframe_for_json(df)[['timestamp','open','high','low','close','volume','ema5','ema10','ema24','ema52']].to_dict('records'),
+        'trend_line': {
+            'offset': int(df.index[-N]),
+            'slope': m,
+            'intercept': b,
+            'length': int(N)
+        }
+    })
+
 @app.route('/')
 def index():
     """主页"""