JamesTang
f30bcdc680
- 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
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
import json
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import random
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import math
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from datetime import datetime, timedelta
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def generate_ohlcv_from_pattern(symbol, date_str, pattern_segments):
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"""
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Generates minute-by-minute OHLCV data based on a list of trend segments.
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Args:
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symbol: Ticker symbol
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date_str: Date (YYYY-MM-DD)
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pattern_segments: List of dicts with:
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- duration_mins: int
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- start_price: float
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- end_price: float
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- volatility: float (standard deviation)
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- volume_mult: float (multiplier of average volume)
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"""
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base_date = datetime.strptime(date_str, "%Y-%m-%d")
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current_time = base_date.replace(hour=9, minute=30)
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data = []
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for segment in pattern_segments:
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duration = segment['duration_mins']
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start_p = segment['start_price']
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end_p = segment['end_price']
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volatility = segment['volatility']
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vol_mult = segment.get('volume_mult', 1.0)
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price_step = (end_p - start_p) / duration
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for i in range(duration):
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trend_price = start_p + (price_step * i)
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# Random walk component
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noise = random.gauss(0, volatility)
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# OHLC generation
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open_p = trend_price + noise
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high_p = open_p + abs(random.gauss(0, volatility/2))
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low_p = open_p - abs(random.gauss(0, volatility/2))
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close_p = (open_p + high_p + low_p) / 3 + random.gauss(0, volatility/4)
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# Ensure logical constraints
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high_p = max(open_p, close_p, high_p)
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low_p = min(open_p, close_p, low_p)
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# Volume profile (U-shaped usually, but modulated by segment)
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time_factor = 1.0
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if current_time.hour < 10 or current_time.hour >= 15:
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time_factor = 1.5
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base_vol = 50000 * time_factor * vol_mult
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volume = int(max(100, random.gauss(base_vol, base_vol * 0.3)))
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data.append({
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"code": symbol,
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"time": current_time.strftime("%Y-%m-%d %H:%M:%S"),
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"open": round(open_p, 2),
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"high": round(high_p, 2),
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"low": round(low_p, 2),
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"close": round(close_p, 2),
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"volume": volume
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})
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current_time += timedelta(minutes=1)
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return data
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if __name__ == "__main__":
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# Example: AAPL "Morning Rally then Chop"
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# Starting at 150.00
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segments = [
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{"duration_mins": 30, "start_price": 150.0, "end_price": 152.5, "volatility": 0.15, "volume_mult": 1.5}, # Rally
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{"duration_mins": 60, "start_price": 152.5, "end_price": 151.8, "volatility": 0.10, "volume_mult": 0.8}, # Pullback
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{"duration_mins": 120, "start_price": 151.8, "end_price": 152.2, "volatility": 0.08, "volume_mult": 0.5}, # Chop
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{"duration_mins": 180, "start_price": 152.2, "end_price": 153.5, "volatility": 0.12, "volume_mult": 1.2}, # Afternoon push
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]
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data = generate_ohlcv_from_pattern("AAPL", "2026-02-06", segments)
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with open("data/mock_scenarios/AAPL_rally.json", "w") as f:
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json.dump(data, f, indent=2)
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print(f"Generated {len(data)} points for AAPL")
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