feat: add market sentiment filters (Fear & Greed, CryptoPanic, CryptoQuant)

- SentimentProvider: fetches Fear & Greed Index (free, no key),
  CryptoPanic news sentiment (free key), CryptoQuant exchange
  netflow (free key)
- SentimentData: aggregated should_buy/should_block logic
  - Fear < 30 = buy opportunity, Greed > 80 = block buying
  - Negative news < -0.5 = block buying
  - Exchange outflow = bullish, inflow = bearish
- Integrated into Asian Session RSI strategy as entry filter
- All providers optional — disabled when API key missing
- 14 sentiment tests + 386 total tests passing

1 files changed, 24 insertions, 18 deletions

diff --git a/services/strategy-engine/tests/test_bollinger_strategy.py b/services/strategy-engine/tests/test_bollinger_strategy.py
index 473d9b4..7761f2d 100644
--- a/services/strategy-engine/tests/test_bollinger_strategy.py
+++ b/services/strategy-engine/tests/test_bollinger_strategy.py
@@ -107,12 +107,14 @@ def test_bollinger_squeeze_detection():
     """Tight bandwidth → no signal during squeeze."""
     # Use a strategy with a high squeeze threshold so constant prices trigger squeeze
     s = BollingerStrategy()
-    s.configure({
-        "period": 5,
-        "num_std": 2.0,
-        "min_bandwidth": 0.0,
-        "squeeze_threshold": 0.5,  # Very high threshold to ensure squeeze triggers
-    })
+    s.configure(
+        {
+            "period": 5,
+            "num_std": 2.0,
+            "min_bandwidth": 0.0,
+            "squeeze_threshold": 0.5,  # Very high threshold to ensure squeeze triggers
+        }
+    )
 
     # Feed identical prices → bandwidth = 0 (below any threshold)
     for _ in range(6):
@@ -126,12 +128,14 @@ def test_bollinger_squeeze_detection():
 def test_bollinger_squeeze_breakout_buy():
     """Squeeze ends with price above SMA → BUY signal."""
     s = BollingerStrategy()
-    s.configure({
-        "period": 5,
-        "num_std": 1.0,
-        "min_bandwidth": 0.0,
-        "squeeze_threshold": 0.01,
-    })
+    s.configure(
+        {
+            "period": 5,
+            "num_std": 1.0,
+            "min_bandwidth": 0.0,
+            "squeeze_threshold": 0.01,
+        }
+    )
 
     # Feed identical prices to create a squeeze (bandwidth = 0)
     for _ in range(6):
@@ -149,12 +153,14 @@ def test_bollinger_squeeze_breakout_buy():
 def test_bollinger_pct_b_conviction():
     """Signals near band extremes have higher conviction via %B."""
     s = BollingerStrategy()
-    s.configure({
-        "period": 5,
-        "num_std": 1.0,
-        "min_bandwidth": 0.0,
-        "squeeze_threshold": 0.0,  # Disable squeeze for this test
-    })
+    s.configure(
+        {
+            "period": 5,
+            "num_std": 1.0,
+            "min_bandwidth": 0.0,
+            "squeeze_threshold": 0.0,  # Disable squeeze for this test
+        }
+    )
 
     # Build up with stable prices
     for _ in range(5):