Initial public release

2026-03-30 13:19:11 +09:00
commit 92a692b63c
116 changed files with 5822 additions and 0 deletions
--- a/hydra/indicator/calculator.py
+++ b/hydra/indicator/calculator.py
@@ -0,0 +1,82 @@
+import time
+import pandas as pd
+import pandas_ta as ta
+from hydra.data.models import Candle
+
+_MIN_CANDLES = 210  # EMA_200 requires at least 200 candles; 210 gives buffer
+
+# Common technical indicators used for trading signals.
+# Uses ta.Study (the current pandas-ta API) instead of the deprecated
+# ta.Strategy("All") which is not available in newer pandas-ta versions.
+_DEFAULT_STUDY = ta.Study(
+    name="hydra",
+    ta=[
+        {"kind": "rsi", "length": 14},
+        {"kind": "ema", "length": 9},
+        {"kind": "ema", "length": 20},
+        {"kind": "ema", "length": 50},
+        {"kind": "ema", "length": 200},
+        {"kind": "sma", "length": 20},
+        {"kind": "sma", "length": 50},
+        {"kind": "macd"},
+        {"kind": "bbands"},
+        {"kind": "atr"},
+        {"kind": "adx"},
+        {"kind": "stoch"},
+        {"kind": "stochrsi"},
+        {"kind": "cci"},
+        {"kind": "willr"},
+        {"kind": "obv"},
+        {"kind": "mfi"},
+        {"kind": "mom"},
+        {"kind": "roc"},
+        {"kind": "tsi"},
+        {"kind": "vwap"},
+        {"kind": "supertrend"},
+        {"kind": "kc"},
+        {"kind": "donchian"},
+        {"kind": "aroon"},
+        {"kind": "ao"},
+        {"kind": "er"},
+    ],
+)
+
+
+class IndicatorCalculator:
+    """Compute technical indicators for a candle list using pandas-ta."""
+
+    def compute(self, candles: list[Candle]) -> dict:
+        """
+        Run a comprehensive set of pandas-ta indicators on candles.
+        Returns {} if fewer than _MIN_CANDLES candles provided.
+        NaN values are converted to None.
+        """
+        if len(candles) < _MIN_CANDLES:
+            return {}
+
+        df = pd.DataFrame([
+            {
+                "open": c.open, "high": c.high,
+                "low": c.low, "close": c.close,
+                "volume": c.volume,
+            }
+            for c in candles
+        ])
+
+        # cores=0 disables multiprocessing (avoids overhead for small DataFrames)
+        df.ta.study(_DEFAULT_STUDY, cores=0)
+
+        last = df.iloc[-1].to_dict()
+        result: dict = {}
+        for key, val in last.items():
+            if key in ("open", "high", "low", "close", "volume"):
+                continue
+            if isinstance(val, float) and pd.isna(val):
+                result[key] = None
+            elif hasattr(val, "item"):          # numpy scalar → Python native
+                result[key] = val.item()
+            else:
+                result[key] = val
+
+        result["calculated_at"] = int(time.time() * 1000)
+        return result