Shift adaptive learning to growth sizing

tests/test_strategy.py

@@ -229,6 +229,40 @@ def test_strategy_uses_fractional_kelly_position_size(make_settings, tmp_path) -
     assert signal.diagnostics["position_notional_usdt"] == settings.max_position_usdt
 
 
+def test_strategy_scales_kelly_with_positive_learning_multiplier(make_settings, tmp_path) -> None:
+    settings = make_settings(tmp_path, max_position_usdt=50, kelly_fraction=0.25, kelly_max_fraction=0.20)
+    strategy = SpotStrategy(settings)
+    ticker = Ticker(
+        symbol="BTCUSDT",
+        last_price=101,
+        bid=100.99,
+        ask=101.01,
+        turnover_24h=10_000_000,
+        volume_24h=1000,
+        change_24h=1.0,
+    )
+    common = dict(
+        symbol="BTCUSDT",
+        candles=_ready_candles(),
+        ticker=ticker,
+        open_positions_for_symbol=0,
+        forecast={"usable": True, "probability_up": 0.62, "volatility_percent": 0.2},
+        account={"equity": 200.0},
+    )
+
+    neutral = strategy.entry_signal(**common)
+    scaled = strategy.entry_signal(
+        **common,
+        learning={"adaptive_rules": {"effective_position_size_multiplier": 1.5}},
+    )
+
+    assert neutral.action == "BUY"
+    assert scaled.action == "BUY"
+    assert scaled.diagnostics["position_sizing"]["risk_multiplier"] > neutral.diagnostics["position_sizing"]["risk_multiplier"]
+    assert scaled.diagnostics["position_notional_usdt"] > neutral.diagnostics["position_notional_usdt"]
+    assert scaled.diagnostics["position_notional_usdt"] <= settings.max_position_usdt
+
+
 def test_strategy_buys_probabilistic_rebound_after_stabilized_drop(make_settings, tmp_path) -> None:
     settings = make_settings(tmp_path, rebound_entry_confidence=0.58, rebound_min_probability=0.58)
     strategy = SpotStrategy(settings)