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Use Torch as the only forecast model

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This commit is contained in:
Codex
2026-06-21 08:37:09 +03:00
parent 25651d7fa7
commit f19856ca6e
7 changed files with 129 additions and 278 deletions
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tests/test_time_series.py
+66 -50
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@@ -34,11 +34,53 @@ def _candles_from_returns(returns: list[float]) -> list[Candle]:
return candles
def test_time_series_forecaster_selects_positive_predictive_model(make_settings, tmp_path) -> None:
def _write_torch_gru_artifact(
path,
*,
head_bias: float,
validation_mae_percent: float = 0.02,
baseline_mae_percent: float = 0.08,
skill: float = 0.2,
) -> None:
hidden_size = 2
path.write_text(
json.dumps(
{
"version": 2,
"type": "pytorch_recurrent_forecaster",
"symbols": {
"BTCUSDT": {
"model": "torch_gru",
"architecture": "gru",
"lookback": 8,
"hidden_size": hidden_size,
"num_layers": 1,
"mean": 0.0,
"scale": 0.001,
"clip": 8.0,
"validation_mae_percent": validation_mae_percent,
"baseline_mae_percent": baseline_mae_percent,
"skill": skill,
"state_dict": {
"weight_ih_l0": [[0.0] for _ in range(3 * hidden_size)],
"weight_hh_l0": [[0.0, 0.0] for _ in range(3 * hidden_size)],
"bias_ih_l0": [0.0 for _ in range(3 * hidden_size)],
"bias_hh_l0": [0.0 for _ in range(3 * hidden_size)],
},
"head_weight": [0.0, 0.0],
"head_bias": head_bias,
},
},
}
),
encoding="utf-8",
)
def test_time_series_forecaster_requires_torch_artifact(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 = []
@@ -47,32 +89,32 @@ def test_time_series_forecaster_selects_positive_predictive_model(make_settings,
value = 0.00025 + value * 0.55
returns.append(value)
forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns))
forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
assert forecast.usable is True
assert forecast.model != "naive"
assert forecast.expected_return_percent > 0
assert forecast.probability_up > 0.5
assert forecast.usable is False
assert forecast.model == "none"
assert forecast.candidates == []
assert "PyTorch" in forecast.reason
def test_time_series_forecaster_blocks_negative_edge(make_settings, tmp_path) -> None:
def test_time_series_forecaster_blocks_negative_torch_edge(make_settings, tmp_path) -> None:
artifact_path = tmp_path / "lstm_forecaster.json"
_write_torch_gru_artifact(artifact_path, head_bias=-0.8, validation_mae_percent=0.01)
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,
time_series_lstm_model_path=artifact_path,
)
returns = []
value = -0.0003
for _ in range(140):
value = -0.00025 + value * 0.55
returns.append(value)
returns = [0.00015 if index % 4 else -0.00005 for index in range(140)]
forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns))
forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
assert forecast.usable is True
assert forecast.model == "torch_gru"
assert forecast.expected_return_percent < 0
assert forecast.probability_up < 0.45
assert forecast.block_entry is True
@@ -93,7 +135,6 @@ def test_time_series_forecaster_ignores_legacy_lstm_artifact(make_settings, tmp_
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,
)
@@ -101,46 +142,18 @@ def test_time_series_forecaster_ignores_legacy_lstm_artifact(make_settings, tmp_
forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
assert forecast.usable is True
assert all(candidate["model"] != "lstm" for candidate in forecast.candidates)
assert forecast.usable is False
assert forecast.model == "none"
assert forecast.candidates == []
assert "PyTorch" in forecast.reason
def test_time_series_forecaster_reads_torch_gru_artifact(make_settings, tmp_path) -> None:
artifact_path = tmp_path / "lstm_forecaster.json"
hidden_size = 2
artifact_path.write_text(
json.dumps(
{
"version": 2,
"type": "pytorch_recurrent_forecaster",
"symbols": {
"BTCUSDT": {
"model": "torch_gru",
"architecture": "gru",
"lookback": 8,
"hidden_size": hidden_size,
"num_layers": 1,
"mean": 0.0,
"scale": 0.001,
"clip": 8.0,
"state_dict": {
"weight_ih_l0": [[0.0] for _ in range(3 * hidden_size)],
"weight_hh_l0": [[0.0, 0.0] for _ in range(3 * hidden_size)],
"bias_ih_l0": [0.0 for _ in range(3 * hidden_size)],
"bias_hh_l0": [0.0 for _ in range(3 * hidden_size)],
},
"head_weight": [0.0, 0.0],
"head_bias": 0.2,
},
},
}
),
encoding="utf-8",
)
_write_torch_gru_artifact(artifact_path, head_bias=0.2)
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,
)
@@ -149,4 +162,7 @@ def test_time_series_forecaster_reads_torch_gru_artifact(make_settings, tmp_path
forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
assert forecast.usable is True
assert any(candidate["model"] == "torch_gru" for candidate in forecast.candidates)
assert forecast.model == "torch_gru"
assert forecast.candidates == [{"model": "torch_gru", "mae_percent": 0.02}]
assert forecast.expected_return_percent > 0
assert forecast.probability_up > 0.5