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