Use Kelly allocation for Torch position scaling

tests/test_strategy.py

@@ -263,7 +263,8 @@ def test_torch_forecast_buys_only_from_positive_torch_edge(make_settings, tmp_pa
     assert signal.action == "BUY"
     assert signal.diagnostics["strategy_mode"] == "torch_forecast"
     assert signal.diagnostics["checks"]["torch_model_ok"] is True
-    assert signal.diagnostics["position_notional_usdt"] == 25.0
+    assert signal.diagnostics["position_sizing"]["method"] == "torch_forecast_fractional_kelly"
+    assert settings.min_position_usdt <= signal.diagnostics["position_notional_usdt"] <= settings.max_position_usdt
 
 
 def test_torch_forecast_blocks_without_valid_torch_model(make_settings, tmp_path) -> None:
@@ -328,6 +329,71 @@ def test_torch_forecast_allows_additional_entries_until_symbol_limit(make_settin
     assert "symbol position limit" in capped.reason
 
 
+def test_torch_forecast_kelly_buys_only_remaining_symbol_allocation(make_settings, tmp_path) -> None:
+    settings = make_settings(
+        tmp_path,
+        strategy_mode="torch_forecast",
+        min_position_usdt=1,
+        max_position_usdt=8,
+        max_symbol_exposure_usdt=25,
+        max_positions_per_symbol=6,
+        stop_loss_percent=0.04,
+        take_profit_percent=0.035,
+        kelly_sizing_enabled=True,
+        kelly_fraction=0.25,
+        kelly_max_fraction=0.20,
+        time_series_min_edge_percent=0.10,
+        time_series_min_probability_up=0.47,
+    )
+    strategy = SpotStrategy(settings)
+    ticker = Ticker("BTCUSDT", 105, 104.99, 105.01, 10_000_000, 1000, 1.0)
+    forecast = {
+        "usable": True,
+        "model": "torch_gru",
+        "expected_return_percent": 0.60,
+        "probability_up": 0.84,
+        "skill": 0.22,
+        "block_entry": False,
+    }
+
+    first = strategy.entry_signal(
+        "BTCUSDT",
+        [],
+        ticker,
+        open_positions_for_symbol=0,
+        forecast=forecast,
+        account={"equity": 100.0, "symbol": "BTCUSDT", "symbol_exposure_usdt": 0.0},
+    )
+    second = strategy.entry_signal(
+        "BTCUSDT",
+        [],
+        ticker,
+        open_positions_for_symbol=1,
+        forecast=forecast,
+        account={"equity": 100.0, "symbol": "BTCUSDT", "symbol_exposure_usdt": 8.0},
+    )
+    filled = strategy.entry_signal(
+        "BTCUSDT",
+        [],
+        ticker,
+        open_positions_for_symbol=2,
+        forecast=forecast,
+        account={"equity": 100.0, "symbol": "BTCUSDT", "symbol_exposure_usdt": 20.0},
+    )
+
+    first_sizing = first.diagnostics["position_sizing"]
+    second_sizing = second.diagnostics["position_sizing"]
+    assert first.action == "BUY"
+    assert first_sizing["method"] == "torch_forecast_fractional_kelly"
+    assert first_sizing["kelly_target_notional_usdt"] > settings.max_position_usdt
+    assert first.diagnostics["position_notional_usdt"] == settings.max_position_usdt
+    assert second.action == "BUY"
+    assert 1 <= second.diagnostics["position_notional_usdt"] < settings.max_position_usdt
+    assert second_sizing["kelly_open_symbol_exposure_usdt"] == 8.0
+    assert filled.action == "HOLD"
+    assert filled.diagnostics["checks"]["risk_size_ok"] is False
+
+
 def test_torch_forecast_blocks_failed_quality_gate(make_settings, tmp_path) -> None:
     settings = make_settings(
         tmp_path,
@@ -362,7 +428,7 @@ def test_torch_forecast_blocks_failed_quality_gate(make_settings, tmp_path) -> N
     assert signal.diagnostics["checks"]["quality_gate_ok"] is False
 
 
-def test_torch_forecast_probe_buys_on_positive_high_probability(make_settings, tmp_path) -> None:
+def test_torch_forecast_probe_blocks_when_kelly_size_is_too_small(make_settings, tmp_path) -> None:
     settings = make_settings(
         tmp_path,
         strategy_mode="torch_forecast",
@@ -395,11 +461,11 @@ def test_torch_forecast_probe_buys_on_positive_high_probability(make_settings, t
         account={"equity": 100.0},
     )
 
-    assert signal.action == "BUY"
+    assert signal.action == "HOLD"
     assert signal.diagnostics["edge_mode"] == "probe"
     assert signal.diagnostics["checks"]["expected_edge_ok"] is True
-    assert signal.diagnostics["position_sizing"]["edge_mode"] == "probe"
-    assert settings.min_position_usdt <= signal.diagnostics["position_notional_usdt"] < settings.max_position_usdt
+    assert signal.diagnostics["checks"]["risk_size_ok"] is False
+    assert signal.diagnostics["position_notional_usdt"] == 0.0
 
 
 def test_torch_forecast_probe_blocks_negative_expected_return(make_settings, tmp_path) -> None:
@@ -436,7 +502,7 @@ def test_torch_forecast_probe_blocks_negative_expected_return(make_settings, tmp
     assert signal.diagnostics["checks"]["expected_edge_ok"] is False
 
 
-def test_torch_forecast_rebound_overlay_buys_stabilized_drop(make_settings, tmp_path) -> None:
+def test_torch_forecast_rebound_overlay_blocks_when_kelly_size_is_too_small(make_settings, tmp_path) -> None:
     settings = make_settings(
         tmp_path,
         strategy_mode="torch_forecast",
@@ -479,12 +545,12 @@ def test_torch_forecast_rebound_overlay_buys_stabilized_drop(make_settings, tmp_
         account={"equity": 100.0},
     )
 
-    assert signal.action == "BUY"
-    assert signal.diagnostics["entry_path"] == "rebound"
+    assert signal.action == "HOLD"
     assert signal.diagnostics["rebound"]["active"] is True
-    assert signal.diagnostics["edge_mode"] == "rebound"
+    assert signal.diagnostics["model_rebound_entry_ok"] is True
+    assert signal.diagnostics["rebound_entry_sized_ok"] is False
     assert signal.diagnostics["checks"]["expected_edge_ok"] is False
-    assert signal.diagnostics["position_notional_usdt"] <= settings.rebound_max_position_usdt
+    assert signal.diagnostics["checks"]["risk_size_ok"] is False
 
 
 def test_torch_forecast_rebound_overlay_does_not_buy_negative_forecast(make_settings, tmp_path) -> None:
@@ -532,7 +598,7 @@ def test_torch_forecast_rebound_overlay_does_not_buy_negative_forecast(make_sett
     assert signal.diagnostics["edge_mode"] == "blocked"
 
 
-def test_torch_forecast_rebound_fallback_buys_when_symbol_has_no_model(make_settings, tmp_path) -> None:
+def test_torch_forecast_rebound_fallback_blocks_when_kelly_size_is_too_small(make_settings, tmp_path) -> None:
     settings = make_settings(
         tmp_path,
         strategy_mode="torch_forecast",
@@ -571,12 +637,11 @@ def test_torch_forecast_rebound_fallback_buys_when_symbol_has_no_model(make_sett
         account={"equity": 100.0},
     )
 
-    assert signal.action == "BUY"
-    assert signal.diagnostics["entry_path"] == "rebound_fallback"
+    assert signal.action == "HOLD"
     assert signal.diagnostics["fallback_rebound_entry_ok"] is True
+    assert signal.diagnostics["rebound_entry_sized_ok"] is False
     assert signal.diagnostics["missing_torch_model"] is True
-    assert signal.diagnostics["edge_mode"] == "rebound_fallback"
-    assert signal.diagnostics["position_notional_usdt"] <= settings.rebound_max_position_usdt
+    assert signal.diagnostics["checks"]["risk_size_ok"] is False
 
 
 def test_torch_forecast_rebound_fallback_can_be_disabled(make_settings, tmp_path) -> None: