Initial TradeBot implementation

tests/test_time_series.py

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+from __future__ import annotations
+
+import json
+
+from crypto_spot_bot.models import Candle
+from crypto_spot_bot.time_series import TimeSeriesForecaster
+
+
+def _candles_from_returns(returns: list[float]) -> list[Candle]:
+    close = 100.0
+    candles = [
+        Candle(
+            timestamp=0,
+            open=close,
+            high=close * 1.001,
+            low=close * 0.999,
+            close=close,
+            volume=100,
+        )
+    ]
+    for index, ret in enumerate(returns, start=1):
+        previous = close
+        close = close * (2.718281828459045 ** ret)
+        candles.append(
+            Candle(
+                timestamp=index,
+                open=previous,
+                high=max(previous, close) * 1.001,
+                low=min(previous, close) * 0.999,
+                close=close,
+                volume=100,
+            )
+        )
+    return candles
+
+
+def test_time_series_forecaster_selects_positive_predictive_model(make_settings, tmp_path) -> None:
+    settings = make_settings(
+        tmp_path,
+        time_series_min_candles=80,
+        time_series_validation_window=24,
+        time_series_forecast_horizon=3,
+    )
+    returns = []
+    value = 0.0003
+    for _ in range(140):
+        value = 0.00025 + value * 0.55
+        returns.append(value)
+
+    forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns))
+
+    assert forecast.usable is True
+    assert forecast.model != "naive"
+    assert forecast.expected_return_percent > 0
+    assert forecast.probability_up > 0.5
+
+
+def test_time_series_forecaster_blocks_negative_edge(make_settings, tmp_path) -> None:
+    settings = make_settings(
+        tmp_path,
+        time_series_min_candles=80,
+        time_series_validation_window=24,
+        time_series_forecast_horizon=3,
+        time_series_min_edge_percent=0.03,
+    )
+    returns = []
+    value = -0.0003
+    for _ in range(140):
+        value = -0.00025 + value * 0.55
+        returns.append(value)
+
+    forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns))
+
+    assert forecast.usable is True
+    assert forecast.expected_return_percent < 0
+    assert forecast.block_entry is True
+
+
+def test_time_series_forecaster_includes_lstm_candidate(make_settings, tmp_path) -> None:
+    settings = make_settings(
+        tmp_path,
+        time_series_min_candles=80,
+        time_series_validation_window=20,
+        time_series_lstm_enabled=True,
+        time_series_lstm_lookback=12,
+        time_series_lstm_units=4,
+    )
+    returns = []
+    for index in range(140):
+        seasonal = 0.00018 if index % 5 in {0, 1, 2} else -0.00011
+        returns.append(seasonal + 0.00002 * ((index % 7) - 3))
+
+    forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
+
+    assert forecast.usable is True
+    assert any(candidate["model"] == "lstm" for candidate in forecast.candidates)
+
+
+def test_time_series_forecaster_reads_lstm_artifact(make_settings, tmp_path) -> None:
+    artifact_path = tmp_path / "lstm_forecaster.json"
+    artifact_path.write_text(
+        json.dumps(
+            {
+                "version": 1,
+                "symbols": {
+                    "BTCUSDT": {"lookback": 10, "units": 3, "ridge": 0.01},
+                },
+            }
+        ),
+        encoding="utf-8",
+    )
+    settings = make_settings(
+        tmp_path,
+        time_series_min_candles=80,
+        time_series_validation_window=20,
+        time_series_lstm_enabled=True,
+        time_series_lstm_model_path=artifact_path,
+    )
+    returns = [0.00012 if index % 3 else -0.00008 for index in range(140)]
+
+    forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
+
+    assert forecast.usable is True
+    assert any(candidate["model"] == "lstm" for candidate in forecast.candidates)