feat: technical, news, and social analysis agents

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-20 17:52:51 +09:00
parent 46e06df131
commit 7e1d556385
6 changed files with 295 additions and 0 deletions
--- a/agents/technical.py
+++ b/agents/technical.py
@@ -0,0 +1,113 @@
+import pandas as pd
+import ta
+import logging
+
+logger = logging.getLogger(__name__)
+
+class TechnicalAgent:
+    MIN_CANDLES = 30
+
+    def analyze(self, df: pd.DataFrame) -> float:
+        if len(df) < self.MIN_CANDLES:
+            return 50.0
+
+        try:
+            signals = []
+
+            # RSI (14)
+            rsi = ta.momentum.RSIIndicator(df["close"], window=14).rsi().iloc[-1]
+            if rsi < 30:
+                signals.append(65)
+            elif rsi < 45:
+                signals.append(70)
+            elif rsi < 55:
+                signals.append(50)
+            elif rsi < 70:
+                signals.append(35)
+            else:
+                signals.append(40)
+
+            # MACD
+            macd_ind = ta.trend.MACD(df["close"])
+            macd_diff = macd_ind.macd_diff().iloc[-1]
+            prev_diff = macd_ind.macd_diff().iloc[-2]
+            if macd_diff > 0 and prev_diff <= 0:
+                signals.append(85)
+            elif macd_diff < 0 and prev_diff >= 0:
+                signals.append(15)
+            elif macd_diff > 0:
+                signals.append(65)
+            else:
+                signals.append(35)
+
+            # Bollinger Bands
+            bb = ta.volatility.BollingerBands(df["close"])
+            bb_high = bb.bollinger_hband().iloc[-1]
+            bb_low = bb.bollinger_lband().iloc[-1]
+            price = df["close"].iloc[-1]
+            bb_pct = (price - bb_low) / (bb_high - bb_low) if (bb_high - bb_low) > 0 else 0.5
+            if bb_pct < 0.2:
+                signals.append(55)
+            elif bb_pct > 0.8:
+                signals.append(40)
+            else:
+                signals.append(50)
+
+            # SMA trend (20 vs 50)
+            sma20 = df["close"].rolling(20).mean().iloc[-1]
+            sma50 = df["close"].rolling(50).mean().iloc[-1] if len(df) >= 50 else sma20
+            if price > sma20 > sma50:
+                signals.append(80)
+            elif price > sma20:
+                signals.append(65)
+            elif price < sma20 < sma50:
+                signals.append(20)
+            else:
+                signals.append(40)
+
+            # SMA 200 (long-term trend)
+            if len(df) >= 200:
+                sma200 = df["close"].rolling(200).mean().iloc[-1]
+                if price > sma200:
+                    signals.append(70)
+                else:
+                    signals.append(30)
+
+            # Volume trend
+            vol_avg = df["volume"].rolling(20).mean().iloc[-1]
+            vol_current = df["volume"].iloc[-1]
+            vol_ratio = vol_current / vol_avg if vol_avg > 0 else 1.0
+            if vol_ratio > 2.0 and price > sma20:
+                signals.append(80)
+            elif vol_ratio > 2.0:
+                signals.append(40)
+            else:
+                signals.append(50)
+
+            # OBV trend
+            obv = ta.volume.OnBalanceVolumeIndicator(df["close"], df["volume"]).on_balance_volume()
+            obv_sma = obv.rolling(20).mean()
+            if obv.iloc[-1] > obv_sma.iloc[-1]:
+                signals.append(65)
+            else:
+                signals.append(35)
+
+            # Candlestick patterns (simplified)
+            last = df.iloc[-1]
+            prev = df.iloc[-2]
+            body = last["close"] - last["open"]
+            prev_body = prev["close"] - prev["open"]
+            if prev_body < 0 and body > 0 and body > abs(prev_body):
+                signals.append(80)  # bullish engulfing
+            elif prev_body > 0 and body < 0 and abs(body) > prev_body:
+                signals.append(20)  # bearish engulfing
+            elif abs(body) < (last["high"] - last["low"]) * 0.1:
+                signals.append(50)  # doji
+            else:
+                signals.append(50)
+
+            return round(sum(signals) / len(signals), 1)
+
+        except Exception as e:
+            logger.error(f"Technical analysis error: {e}")
+            return 50.0