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, 22 insertions, 8 deletions

diff --git a/services/order-executor/src/order_executor/risk_manager.py b/services/order-executor/src/order_executor/risk_manager.py
index 94d15c2..5a05746 100644
--- a/services/order-executor/src/order_executor/risk_manager.py
+++ b/services/order-executor/src/order_executor/risk_manager.py
@@ -212,8 +212,16 @@ class RiskManager:
         prices_a = prices_a[-min_len:]
         prices_b = prices_b[-min_len:]
 
-        returns_a = [(prices_a[i] - prices_a[i-1]) / prices_a[i-1] for i in range(1, len(prices_a)) if prices_a[i-1] != 0]
-        returns_b = [(prices_b[i] - prices_b[i-1]) / prices_b[i-1] for i in range(1, len(prices_b)) if prices_b[i-1] != 0]
+        returns_a = [
+            (prices_a[i] - prices_a[i - 1]) / prices_a[i - 1]
+            for i in range(1, len(prices_a))
+            if prices_a[i - 1] != 0
+        ]
+        returns_b = [
+            (prices_b[i] - prices_b[i - 1]) / prices_b[i - 1]
+            for i in range(1, len(prices_b))
+            if prices_b[i - 1] != 0
+        ]
 
         if len(returns_a) < 3 or len(returns_b) < 3:
             return None
@@ -225,7 +233,9 @@ class RiskManager:
         mean_a = sum(returns_a) / len(returns_a)
         mean_b = sum(returns_b) / len(returns_b)
 
-        cov = sum((a - mean_a) * (b - mean_b) for a, b in zip(returns_a, returns_b)) / len(returns_a)
+        cov = sum((a - mean_a) * (b - mean_b) for a, b in zip(returns_a, returns_b)) / len(
+            returns_a
+        )
         std_a = math.sqrt(sum((a - mean_a) ** 2 for a in returns_a) / len(returns_a))
         std_b = math.sqrt(sum((b - mean_b) ** 2 for b in returns_b) / len(returns_b))
 
@@ -253,7 +263,11 @@ class RiskManager:
             if not hist or len(hist) < 5:
                 continue
             prices = list(hist)
-            returns = [(prices[i] - prices[i-1]) / prices[i-1] for i in range(1, len(prices)) if prices[i-1] != 0]
+            returns = [
+                (prices[i] - prices[i - 1]) / prices[i - 1]
+                for i in range(1, len(prices))
+                if prices[i - 1] != 0
+            ]
             if returns:
                 all_returns.append(returns)
                 weight = float(pos.quantity * pos.current_price / balance)
@@ -280,15 +294,15 @@ class RiskManager:
 
         return abs(var_return) * 100  # As percentage
 
-    def check_portfolio_exposure(self, positions: dict[str, Position], balance: Decimal) -> RiskCheckResult:
+    def check_portfolio_exposure(
+        self, positions: dict[str, Position], balance: Decimal
+    ) -> RiskCheckResult:
         """Check total portfolio exposure."""
         if balance <= 0:
             return RiskCheckResult(allowed=True, reason="OK")
 
         total_exposure = sum(
-            pos.quantity * pos.current_price
-            for pos in positions.values()
-            if pos.quantity > 0
+            pos.quantity * pos.current_price for pos in positions.values() if pos.quantity > 0
         )
 
         exposure_ratio = total_exposure / balance