feat: train forecasts on trade outcomes

tests/test_trade_objective_training.py

@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import math
+
+import pytest
+import torch
+
+from crypto_spot_bot.models import Candle
+from crypto_spot_bot.time_series import _torch_head_outputs
+from tools.train_torch_recurrent_forecaster import (
+    OUTPUT_LAYOUT,
+    RecurrentReturnModel,
+    _barrier_outcome,
+    _export_head_state,
+)
+
+
+def _candle(index: int, *, open_: float, high: float, low: float, close: float) -> Candle:
+    return Candle(index, open_, high, low, close, 100.0)
+
+
+def test_barrier_target_uses_next_open_and_marks_take_profit_first() -> None:
+    candles = [
+        _candle(0, open_=90.0, high=101.0, low=89.0, close=100.0),
+        _candle(1, open_=100.0, high=102.0, low=99.0, close=101.0),
+        _candle(2, open_=101.0, high=104.0, low=100.0, close=103.0),
+    ]
+
+    net_return, event = _barrier_outcome(
+        candles,
+        end_index=0,
+        horizon=2,
+        stop_loss_percent=0.02,
+        take_profit_percent=0.03,
+        round_trip_cost=0.002,
+    ) or (math.nan, math.nan)
+
+    assert event == 1.0
+    assert net_return == pytest.approx(math.log(1.03) - 0.002)
+
+
+def test_barrier_target_resolves_same_candle_tie_as_stop_loss() -> None:
+    candles = [
+        _candle(0, open_=100.0, high=101.0, low=99.0, close=100.0),
+        _candle(1, open_=100.0, high=104.0, low=97.0, close=101.0),
+    ]
+
+    net_return, event = _barrier_outcome(
+        candles,
+        end_index=0,
+        horizon=1,
+        stop_loss_percent=0.02,
+        take_profit_percent=0.03,
+        round_trip_cost=0.002,
+    ) or (math.nan, math.nan)
+
+    assert event == 0.0
+    assert net_return == pytest.approx(math.log(0.98) - 0.002)
+
+
+def test_multitask_head_export_matches_runtime_inference() -> None:
+    torch.manual_seed(7)
+    model = RecurrentReturnModel(
+        architecture="gru",
+        input_size=2,
+        hidden_size=4,
+        num_layers=1,
+        dropout=0.0,
+        output_size=2 * len(OUTPUT_LAYOUT),
+        attention_pooling=False,
+        context_norm=False,
+        multitask_head=True,
+        head_hidden_size=6,
+    )
+    model.eval()
+    context = torch.tensor([[0.2, -0.1, 0.4, 0.3]], dtype=torch.float32)
+    with torch.no_grad():
+        shared = model.head_activation(model.head_hidden(context))
+        returns = model.return_head(shared).view(1, 2, 4)
+        events = model.event_head(shared).view(1, 2, 1)
+        expected = torch.cat((returns, events), dim=2).reshape(-1).tolist()
+
+    entry = {"multitask_head": True, **_export_head_state(model)}
+    actual = _torch_head_outputs(context[0].tolist(), entry, hidden_size=4)
+
+    assert actual == pytest.approx(expected, abs=2e-6)