feat: Integrated Dashboard v2.0 with LLM-driven Mock Backend

- Promoted prototype dashboard to main app - Added /api/market-data endpoint - Implemented market_simulator.py for realistic OHLCV generation - Updated MoomooClient to serve simulated scenarios - Wired frontend to backend API
2026-02-07 06:34:19 +08:00
parent dfb7b9e619
commit f30bcdc680
8 changed files with 5796 additions and 47 deletions
--- a/data/utils/market_simulator.py
+++ b/data/utils/market_simulator.py
@@ -0,0 +1,87 @@
+import json
+import random
+import math
+from datetime import datetime, timedelta
+
+def generate_ohlcv_from_pattern(symbol, date_str, pattern_segments):
+    """
+    Generates minute-by-minute OHLCV data based on a list of trend segments.
+    
+    Args:
+        symbol: Ticker symbol
+        date_str: Date (YYYY-MM-DD)
+        pattern_segments: List of dicts with:
+            - duration_mins: int
+            - start_price: float
+            - end_price: float
+            - volatility: float (standard deviation)
+            - volume_mult: float (multiplier of average volume)
+    """
+    base_date = datetime.strptime(date_str, "%Y-%m-%d")
+    current_time = base_date.replace(hour=9, minute=30)
+    
+    data = []
+    
+    for segment in pattern_segments:
+        duration = segment['duration_mins']
+        start_p = segment['start_price']
+        end_p = segment['end_price']
+        volatility = segment['volatility']
+        vol_mult = segment.get('volume_mult', 1.0)
+        
+        price_step = (end_p - start_p) / duration
+        
+        for i in range(duration):
+            trend_price = start_p + (price_step * i)
+            
+            # Random walk component
+            noise = random.gauss(0, volatility)
+            
+            # OHLC generation
+            open_p = trend_price + noise
+            high_p = open_p + abs(random.gauss(0, volatility/2))
+            low_p = open_p - abs(random.gauss(0, volatility/2))
+            close_p = (open_p + high_p + low_p) / 3 + random.gauss(0, volatility/4)
+            
+            # Ensure logical constraints
+            high_p = max(open_p, close_p, high_p)
+            low_p = min(open_p, close_p, low_p)
+            
+            # Volume profile (U-shaped usually, but modulated by segment)
+            time_factor = 1.0
+            if current_time.hour < 10 or current_time.hour >= 15:
+                time_factor = 1.5
+                
+            base_vol = 50000 * time_factor * vol_mult
+            volume = int(max(100, random.gauss(base_vol, base_vol * 0.3)))
+            
+            data.append({
+                "code": symbol,
+                "time": current_time.strftime("%Y-%m-%d %H:%M:%S"),
+                "open": round(open_p, 2),
+                "high": round(high_p, 2),
+                "low": round(low_p, 2),
+                "close": round(close_p, 2),
+                "volume": volume
+            })
+            
+            current_time += timedelta(minutes=1)
+            
+    return data
+
+if __name__ == "__main__":
+    # Example: AAPL "Morning Rally then Chop"
+    # Starting at 150.00
+    segments = [
+        {"duration_mins": 30, "start_price": 150.0, "end_price": 152.5, "volatility": 0.15, "volume_mult": 1.5}, # Rally
+        {"duration_mins": 60, "start_price": 152.5, "end_price": 151.8, "volatility": 0.10, "volume_mult": 0.8}, # Pullback
+        {"duration_mins": 120, "start_price": 151.8, "end_price": 152.2, "volatility": 0.08, "volume_mult": 0.5}, # Chop
+        {"duration_mins": 180, "start_price": 152.2, "end_price": 153.5, "volatility": 0.12, "volume_mult": 1.2}, # Afternoon push
+    ]
+    
+    data = generate_ohlcv_from_pattern("AAPL", "2026-02-06", segments)
+    
+    with open("data/mock_scenarios/AAPL_rally.json", "w") as f:
+        json.dump(data, f, indent=2)
+        
+    print(f"Generated {len(data)} points for AAPL")