Initial public release

tests/test_indicator_calculator.py

@@ -0,0 +1,57 @@
+import math
+import pytest
+from hydra.data.models import Candle
+from hydra.indicator.calculator import IndicatorCalculator
+
+
+def _make_candles(n: int) -> list[Candle]:
+    candles = []
+    price = 50000.0
+    for i in range(n):
+        open_ = price
+        high = price * 1.001
+        low = price * 0.999
+        close = price * (1 + (0.001 if i % 2 == 0 else -0.001))
+        price = close
+        candles.append(Candle(
+            market="binance", symbol="BTC/USDT", timeframe="1m",
+            open_time=1_000_000 + i * 60_000,
+            open=open_, high=high, low=low, close=close,
+            volume=100.0 + i,
+            close_time=1_000_000 + i * 60_000 + 59_999,
+        ))
+    return candles
+
+
+def test_compute_returns_dict_with_rsi():
+    calc = IndicatorCalculator()
+    candles = _make_candles(250)
+    result = calc.compute(candles)
+    assert isinstance(result, dict)
+    assert "RSI_14" in result
+    assert isinstance(result["RSI_14"], float), "RSI_14 must be a valid float with 250 candles"
+
+
+def test_compute_no_nan_values():
+    calc = IndicatorCalculator()
+    candles = _make_candles(250)
+    result = calc.compute(candles)
+    for key, val in result.items():
+        if val is not None:
+            assert not (isinstance(val, float) and math.isnan(val)), \
+                f"NaN found for key {key}"
+
+
+def test_compute_returns_empty_for_insufficient_candles():
+    calc = IndicatorCalculator()
+    candles = _make_candles(100)  # fewer than 210
+    result = calc.compute(candles)
+    assert result == {}
+
+
+def test_compute_includes_calculated_at():
+    calc = IndicatorCalculator()
+    candles = _make_candles(250)
+    result = calc.compute(candles)
+    assert "calculated_at" in result
+    assert isinstance(result["calculated_at"], int)