718 lines
30 KiB
Python
718 lines
30 KiB
Python
import pandas as pd
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import requests
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import os
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import csv
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import shutil
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import time
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from datetime import datetime
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import yfinance as yf
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from ta.trend import EMAIndicator
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from ta.momentum import StochasticOscillator
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import math
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class DataEngine:
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def __init__(self, symbol=None, url=None, provider=None, data_dir='data_cache'):
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# 1. Clean and set the symbol
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self.symbol = symbol.strip().upper() if symbol else None
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self.file_path = f"data_cache/{self.symbol}.csv"
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# 2. Setup centralized paths
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base_path = os.path.dirname(os.path.abspath(__file__))
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self.cache_dir = os.path.join(base_path, data_dir)
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os.makedirs(self.cache_dir, exist_ok=True)
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# 3. Load master instrument list
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self.master_instruments = self.load_instruments_from_csv('instruments.csv')
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# 4. Resolve Config (Priority: CSV > Arguments > Yahoo Fallback)
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config = next((i for i in self.master_instruments if i['symbol'].upper() == self.symbol), None)
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if config:
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self.url = config['url']
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self.provider = config['provider']
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elif url:
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self.url = url
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self.provider = provider or 'yahoo'
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elif self.symbol:
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# Automatic Fallback for missing tickers
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self.url = f"https://query1.finance.yahoo.com/v8/finance/chart/{self.symbol}?interval=1d&range=2y"
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self.provider = 'yahoo'
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else:
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self.url = None
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self.provider = None
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# 5. Define file path and auto-sync
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self.file_path = os.path.join(self.cache_dir, f"{self.symbol}.csv") if self.symbol else None
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# This now handles the "24-hour check" automatically
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if self.symbol:
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self.ensure_data()
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def ensure_data(self):
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"""Checks if file exists and is fresh (less than 24h old)."""
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CACHE_EXPIRY = 24 * 3600 # 24 hours
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if os.path.exists(self.file_path):
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# NEW: Check how old the file is
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file_age = time.time() - os.path.getmtime(self.file_path)
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if file_age < CACHE_EXPIRY:
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return True # Data is actually fresh
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else:
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print(f"DEBUG: {self.symbol} cache is stale ({round(file_age/3600)}h old). Refreshing...")
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else:
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print(f"DEBUG: {self.symbol} not found in cache. Attempting download...")
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# If we reached here, it means we either have NO file or a STALE file
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# Instead of just yfinance, call your specialized fetch_data()
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# which uses the URLs from your TEMPLATES
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return self.fetch_data()
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def load_instruments_from_csv(self, file_path):
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instruments = []
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# Dynamic templates based on your preference
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TEMPLATES = {
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'jpm': "https://am.jpmorgan.com/FundsMarketingHandler/historicalData?cusip={cusip}&country=hk&role=per",
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'yahoo': "https://query1.finance.yahoo.com/v8/finance/chart/{cusip}?period1=0&period2=9999999999&interval=1d&events=history",
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'agi': "https://markets.ft.com/data/funds/tearsheet/historical?s={cusip}"
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}
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try:
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abs_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_path)
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if not os.path.exists(abs_path):
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return []
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with open(abs_path, mode='r', encoding='utf-8-sig') as csvfile:
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reader = csv.DictReader(csvfile)
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reader.fieldnames = [name.strip().lower() for name in reader.fieldnames]
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for row in reader:
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symbol = (row.get('symbol') or '').strip().upper()
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cusip = (row.get('cusip') or '').strip()
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provider = (row.get('provider') or 'yahoo').strip().lower()
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if symbol and cusip:
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# Build URL from template
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template = TEMPLATES.get(provider, TEMPLATES['yahoo'])
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url = template.format(cusip=cusip)
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instruments.append({
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"symbol": symbol,
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"url": url,
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"provider": provider,
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"cusip": cusip
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})
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except Exception as e:
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print(f"CRITICAL: Failed to load instruments.csv: {e}")
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return instruments
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def _ensure_data_exists(self):
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if not os.path.exists(self.file_path):
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# Check if this symbol exists in our master CSV mapping
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match = next((i for i in self.instruments if i['symbol'].upper() == self.symbol), None)
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if match:
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print(f"DEBUG: Found {self.symbol} in master list. Fetching from {match['provider']}...")
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self._download_from_provider(match)
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else:
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print(f"DEBUG: {self.symbol} not in master list. Trying generic Yahoo Finance...")
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self._download_generic_yahoo()
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def _download_generic_yahoo(self):
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"""Standard yfinance fallback"""
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try:
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df = yf.download(self.symbol, period="max")
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if not df.empty:
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df.reset_index(inplace=True)
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df.columns = [c.lower() for c in df.columns]
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df.to_csv(self.file_path, index=False)
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except Exception as e:
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print(f"Yahoo fallback failed: {e}")
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def global_sync(self):
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"""Backup, Sync all instruments, and return a summary report."""
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# 1. Run Maintenance/Backup
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self.run_pre_sync_maintenance()
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# FIX 1: Add 'self.' so it calls the method inside this class
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instruments = self.load_instruments_from_csv('instruments.csv')
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report = {
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"total": len(instruments),
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"updated": 0,
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"failed": 0,
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"details": []
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}
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for item in instruments:
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try:
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self.symbol = item['symbol']
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self.provider = item['provider']
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self.url = item['url']
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# FIX 2: Use 'self.cache_dir' to match your __init__ logic
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self.file_path = os.path.join(self.cache_dir, f"{self.symbol}.csv")
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print(f"Updating {self.symbol}...")
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# fetch_data now returns the updated DataFrame or None
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result_df = self.fetch_data()
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time.sleep(1)
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if result_df is not None and not result_df.empty:
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report["updated"] += 1
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last_price = result_df['close'].iloc[-1]
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report["details"].append(f"✅ {self.symbol}: Updated (Price: {last_price})")
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else:
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report["failed"] += 1
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report["details"].append(f"❌ {self.symbol}: No new data found")
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except Exception as e:
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report["failed"] += 1
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report["details"].append(f"⚠️ {self.symbol}: Error ({str(e)})")
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return report
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def run_pre_sync_maintenance(self):
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"""Backs up files and reports current data health."""
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import os
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import shutil
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import pandas as pd
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from datetime import datetime
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# 1. Setup paths correctly
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base_dir = os.path.dirname(os.path.abspath(__file__))
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backup_dir = os.path.join(base_dir, 'backups')
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# 2. Create the timestamped folder path FIRST
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timestamp = datetime.now().strftime("%Y%m%d_%H%M")
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current_backup_path = os.path.join(backup_dir, f"sync_backup_{timestamp}")
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# 3. Create the directories (safety-first)
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os.makedirs(current_backup_path, exist_ok=True)
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print(f"\n--- Pre-Sync Health Check ({timestamp}) ---")
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stats = []
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# 4. Check if cache exists to avoid errors
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if not os.path.exists(self.cache_dir):
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print(f"⚠️ Cache directory not found at {self.cache_dir}")
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return pd.DataFrame()
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# 5. Backup loop
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for filename in os.listdir(self.cache_dir):
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if filename.endswith(".csv"):
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src = os.path.join(self.cache_dir, filename)
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dst = os.path.join(current_backup_path, filename)
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try:
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# Perform copy
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shutil.copy2(src, dst)
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# Read data for health check
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df = pd.read_csv(src)
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# Store stats
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stats.append({
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"Fund": filename.replace(".csv", ""),
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"Rows": len(df),
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"Start": df['date'].min() if 'date' in df.columns else "N/A",
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"End": df['date'].max() if 'date' in df.columns else "N/A"
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})
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print(f"📦 Backed up: {filename} ({len(df)} rows)")
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except Exception as e:
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print(f"⚠️ Could not backup {filename}: {e}")
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continue
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# 6. Display and return report
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if stats:
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stats_df = pd.DataFrame(stats)
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print("\n" + stats_df.to_string(index=False))
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print(f"\n✅ All backups saved to: {current_backup_path}")
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return stats_df
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else:
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print("📭 No CSV files found to backup.")
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return pd.DataFrame()
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def _parse_jpm(self, json_data):
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if isinstance(json_data, dict) and "historicalNAVList" in json_data:
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df = pd.DataFrame(json_data["historicalNAVList"])
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return df.rename(columns={'navPrice': 'close', 'date': 'date'})
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return None
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def _parse_ft_html(self, html_text):
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try:
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# 1. Use BeautifulSoup to handle the nested spans in the Date column
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from bs4 import BeautifulSoup
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soup = BeautifulSoup(html_text, 'html.parser')
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# Find the specific results table
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table = soup.find('table', class_='mod-tearsheet-historical-prices__results')
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if not table:
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print(f"❌ Could not find the results table in the HTML for {self.symbol}")
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return None
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data = []
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rows = table.find('tbody').find_all('tr')
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for row in rows:
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cols = row.find_all('td')
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if len(cols) >= 5:
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# The Date cell has two spans. We'll take the first one (Full date).
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date_cell = cols[0].find('span', class_='mod-ui-hide-small-below')
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date_str = date_cell.get_text(strip=True) if date_cell else cols[0].get_text(strip=True)
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# The Close price is usually the 5th column (index 4)
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close_str = cols[4].get_text(strip=True).replace(',', '')
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data.append({
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'date': date_str,
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'close': close_str
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})
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# 2. Convert to DataFrame
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df = pd.DataFrame(data)
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if df.empty:
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return None
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# 3. Final Type Conversion
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df['date'] = pd.to_datetime(df['date'], errors='coerce')
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df['close'] = pd.to_numeric(df['close'], errors='coerce')
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return df.dropna().sort_values('date').reset_index(drop=True)
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except Exception as e:
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print(f"❌ Failed to parse FT HTML structure: {e}")
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return None
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def _parse_yahoo(self, json_data):
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"""Parses Yahoo Finance v8 Chart JSON"""
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try:
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chart = json_data['chart']['result'][0]
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timestamps = chart['timestamp']
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indicators = chart['indicators']['quote'][0]
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# Use adjclose if available, otherwise close
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closes = indicators.get('close', [])
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df = pd.DataFrame({
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'date': pd.to_datetime(timestamps, unit='s'),
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'close': closes
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})
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return df
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except:
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return None
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def fetch_data(self):
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local_df = pd.DataFrame()
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CACHE_EXPIRY = 24 * 3600
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file_exists = os.path.exists(self.file_path)
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# 1. Load Local Cache & Check Age
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needs_refresh = True
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if file_exists:
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try:
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local_df = pd.read_csv(self.file_path)
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local_df['date'] = pd.to_datetime(local_df['date'], errors='coerce')
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file_age = time.time() - os.path.getmtime(self.file_path)
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if file_age < CACHE_EXPIRY:
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needs_refresh = False
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print(f"🚀 Using Cache: {self.symbol} ({round(file_age/3600, 1)}h old).")
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except Exception as e:
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print(f"⚠️ Cache read error for {self.symbol}: {e}")
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# 2. Network Fetch
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if needs_refresh:
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try:
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if not self.url or str(self.url).lower() == 'none':
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print(f"❌ No URL found for {self.symbol}.")
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return local_df
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print(f"📡 Syncing {self.symbol} from {self.provider}...")
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headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'}
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response = requests.get(self.url, headers=headers, timeout=15)
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if response.status_code == 200:
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new_df = None
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# --- PROVIDER ROUTING ---
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# ROUTING TO PARSERS
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if self.provider == 'yahoo':
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new_df = self._parse_yahoo(response.json())
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elif self.provider == 'jpm':
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new_df = self._parse_jpm(response.json())
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elif self.provider in ['agi', 'ft']:
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new_df = self._parse_ft_html(response.text)
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# --- MERGE & SAVE ---
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if new_df is not None and not new_df.empty:
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new_df['date'] = pd.to_datetime(new_df['date'], errors='coerce')
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combined_df = pd.concat([local_df, new_df], ignore_index=True)
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combined_df = combined_df.sort_values('date').drop_duplicates(subset=['date'], keep='last')
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final_df = combined_df.dropna(subset=['date', 'close'])
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final_df[['date', 'close']].to_csv(self.file_path, index=False)
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print(f"✅ {self.symbol} updated to {final_df['date'].max().date()}")
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return final_df
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else:
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print(f"⚠️ Could not parse data for {self.symbol} via {self.provider}")
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else:
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print(f"⚠️ {self.provider} returned status {response.status_code}")
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except Exception as e:
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print(f"❌ Sync failed for {self.symbol}: {e}")
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return local_df
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def get_local_metrics(self):
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"""Reads ONLY from local CSV and returns metrics immediately."""
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if not os.path.exists(self.file_path):
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return {"error": "Missing Local Data", "status": "needs_sync"}
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try:
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df = pd.read_csv(self.file_path)
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# Ensure columns are clean
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df.columns = [c.lower().strip() for c in df.columns]
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df['date'] = pd.to_datetime(df['date'], errors='coerce')
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df = df.dropna(subset=['date', 'close']).sort_values('date')
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# Pass this local dataframe to your existing calculation function
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return self.calculate_table_metrics(df)
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except Exception as e:
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print(f"Error reading local data for {self.symbol}: {e}")
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return None
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def calculate_table_metrics(self, df):
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if df is None or df.empty or len(df) < 2:
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return None
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# Get the last row for price and date
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last_row = df.iloc[-1]
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last_close = float(last_row['close'])
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# 1. Extract and format the date
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# Handles both datetime objects and string dates
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last_date = last_row['date']
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if hasattr(last_date, 'strftime'):
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formatted_date = last_date.strftime('%Y-%m-%d')
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else:
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formatted_date = str(last_date).split(' ')[0] # Fallback for strings
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prev_close = float(df.iloc[-2]['close'])
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change_pct = ((last_close - prev_close) / prev_close) * 100
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count = len(df)
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def get_ema_offset(window):
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if count >= window:
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from ta.trend import EMAIndicator
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ema = EMAIndicator(close=df['close'], window=window).ema_indicator().iloc[-1]
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return round(((last_close / ema) * 100) - 100, 1)
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return "N/A"
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k_val = d_val = "N/A"
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if count >= 14:
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from ta.momentum import StochasticOscillator
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# Use high/low columns if they exist, otherwise fallback to close for Stoch
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high_src = df['high'] if 'high' in df.columns else df['close']
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low_src = df['low'] if 'low' in df.columns else df['close']
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stoch = StochasticOscillator(high=high_src, low=low_src, close=df['close'], window=14)
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k_val = round(stoch.stoch().iloc[-1], 0)
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d_val = round(stoch.stoch_signal().iloc[-1], 0)
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return {
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"symbol": self.symbol,
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"last_date": formatted_date, # <--- New field added
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"last_close": round(last_close, 2),
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"change_pct": round(change_pct, 2),
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"low_52": round(float(df.tail(252)['close'].min()), 2),
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"high_52": round(float(df.tail(252)['close'].max()), 2),
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"last_ema20": get_ema_offset(20),
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"last_ema50": get_ema_offset(50),
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"last_ema100": get_ema_offset(100),
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"last_ema200": get_ema_offset(200),
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"kd_values": f"{int(k_val)}/{int(d_val)}" if k_val != "N/A" else "N/A"
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}
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class StrategyEngine:
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"""
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Handles financial strategy simulations and backtesting.
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This class takes a DataEngine instance to access files.
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"""
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def __init__(self, data_engine):
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# 1. Save the engine object (The 'Supplier')
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self.data_engine = data_engine
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# 2. Extract the symbol from the supplier so the chef knows the name
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# We don't need .strip() here because DataEngine already did it!
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self.symbol = data_engine.symbol
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def _find_file(self):
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# Try the uppercase version first
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upper_path = os.path.join(self.data_dir, f"{self.symbol}.csv")
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# Try the lowercase version second
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lower_path = os.path.join(self.data_dir, f"{self.symbol.lower()}.csv")
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if os.path.exists(upper_path):
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return upper_path
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elif os.path.exists(lower_path):
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return lower_path
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# If neither exists, print a very specific message to your terminal
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print(f"ERROR: Searched for {upper_path} AND {lower_path} - Neither found!")
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return None
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def load_data(self):
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df = pd.read_csv(self.file_path)
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# Standardize column names to lowercase to avoid 'Price' vs 'price' issues
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df.columns = [c.lower() for c in df.columns]
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# Map common variations to a single 'price' column
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if 'adj close' in df.columns:
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|
df = df.rename(columns={'adj close': 'close'})
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elif 'close' in df.columns:
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df = df.rename(columns={'close': 'close'})
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|
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|
return df
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|
|
|
def calculate_va_vs_dca(self, initial_inv, monthly_target, start_date, allow_sell=True, allow_fractional=True):
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|
import math
|
|
|
|
# 1. Load and Prepare Data
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df = pd.read_csv(self.data_engine.file_path)
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df['date'] = pd.to_datetime(df['date'])
|
|
df = df.sort_values('date')
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|
|
|
# 2. Identify the "Anchor Day" and the "Absolute Latest Day"
|
|
start_dt_obj = pd.to_datetime(start_date)
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|
anchor_day = start_dt_obj.day
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|
latest_csv_date = df['date'].max() # This captures 2026-01-27
|
|
|
|
# --- 3. Filter data starting from your start_date ---
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|
df_from_start = df[df['date'] >= start_dt_obj].copy()
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|
if df_from_start.empty:
|
|
return []
|
|
|
|
# --- 4. Choose rows based on Frequency ---
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|
if frequency == "Weekly":
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|
# Take every 5th trading day
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|
final_df = df_from_start.iloc[::5].copy()
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|
step_increment = float((monthly_goal * 12) / 52)
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|
|
|
elif frequency == "Bi-Weekly":
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|
# Take every 10th trading day
|
|
final_df = df_from_start.iloc[::10].copy()
|
|
step_increment = float((monthly_goal * 12) / 26)
|
|
|
|
else: # Default to Monthly
|
|
# Group by year/month and take the first available day >= anchor_day
|
|
final_df = df_from_start[df_from_start['date'].dt.day >= anchor_day].groupby([
|
|
df_from_start['date'].dt.year,
|
|
df_from_start['date'].dt.month
|
|
], as_index=False).first()
|
|
step_increment = float(monthly_goal)
|
|
|
|
# --- 5. FORCE LAST ROW SAFETY ---
|
|
# Reset index to make concatenation clean
|
|
final_df = final_df.reset_index(drop=True)
|
|
|
|
if not df_from_start.empty:
|
|
latest_row = df_from_start.iloc[[-1]] # Gets the absolute last available day
|
|
if final_df.empty or latest_row.iloc[0]['date'] != final_df.iloc[-1]['date']:
|
|
final_df = pd.concat([final_df, latest_row]).drop_duplicates(subset=['date'])
|
|
|
|
# Sort and final index reset before the loop
|
|
final_df = final_df.sort_values('date').reset_index(drop=True)
|
|
|
|
if final_df.empty:
|
|
return []
|
|
|
|
# --- 6. Helper for share calculation ---
|
|
def get_shares(cash, prc):
|
|
if prc <= 0: return 0
|
|
# Note: allow_fractional should be defined in your class/scope
|
|
return cash / prc if allow_fractional else math.floor(cash / prc)
|
|
|
|
# --- 7. Initial Setup ---
|
|
va_shares = 0
|
|
dca_shares = 0
|
|
va_invested = 0
|
|
dca_invested = 0
|
|
va_target_value = 0
|
|
history = []
|
|
|
|
# --- 8. Strategy Loop ---
|
|
for step, (idx, row) in enumerate(final_df.iterrows()):
|
|
actual_date_str = row['date'].strftime('%Y-%m-%d')
|
|
price = float(row['close'])
|
|
|
|
if step == 0:
|
|
# --- STEP 0: INITIAL DEPOSIT ---
|
|
actual_inv = initial_inv
|
|
dca_actual_inv = initial_inv
|
|
|
|
va_target_value = initial_inv
|
|
va_new_shares = get_shares(actual_inv, price)
|
|
dca_new_shares = va_new_shares
|
|
|
|
else:
|
|
# --- STEP 1+: DVA vs DCA ---
|
|
# Use step_increment (calculated in your frequency logic)
|
|
# to ensure growth matches frequency (Weekly/Monthly)
|
|
current_increment = step_increment
|
|
|
|
# DCA Logic
|
|
dca_actual_inv = current_increment
|
|
dca_new_shares = get_shares(dca_actual_inv, price)
|
|
|
|
# DVA Logic (Fixed Value Path)
|
|
va_target_value += current_increment
|
|
|
|
# Gap calculation: Target vs. current value BEFORE this step's investment
|
|
current_va_val_pre = va_shares * price
|
|
diff = va_target_value - current_va_val_pre
|
|
|
|
# Apply Buy/Sell constraints
|
|
# note: allow_sell should be defined in your class/scope
|
|
actual_inv = diff if (diff >= 0 or allow_sell) else 0
|
|
va_new_shares = get_shares(actual_inv, price)
|
|
|
|
# --- STATE UPDATES ---
|
|
va_shares += va_new_shares
|
|
dca_shares += dca_new_shares
|
|
|
|
va_invested += actual_inv
|
|
dca_invested += dca_actual_inv
|
|
|
|
# --- Unified History Append ---
|
|
history.append({
|
|
"date": actual_date_str,
|
|
"price": round(price, 2),
|
|
"dca_value": round(dca_shares * price, 2),
|
|
"dca_invested": round(dca_invested, 2),
|
|
"dca_shares_trans": round(dca_new_shares, 4),
|
|
"dca_shares_total": round(dca_shares, 4),
|
|
"va_value": round(va_shares * price, 2),
|
|
"va_invested": round(va_invested, 2),
|
|
"va_diff": round(actual_inv, 2),
|
|
"va_shares_trans": round(va_new_shares, 4),
|
|
"va_shares_total": round(va_shares, 4),
|
|
"va_target_value": round(va_target_value, 2)
|
|
})
|
|
|
|
return history
|
|
|
|
def run_simulation(self, start_date, monthly_goal, initial_inv, frequency="Monthly", allow_sell=True, allow_fractional=True):
|
|
# 1. DATA LOADING
|
|
df = pd.read_csv(self.data_engine.file_path)
|
|
df['date'] = pd.to_datetime(df['date'])
|
|
df = df.sort_values('date')
|
|
|
|
# 2. DATE PREP
|
|
start_dt_obj = pd.to_datetime(start_date)
|
|
latest_csv_date = df['date'].max()
|
|
|
|
# Filter data starting from user's choice
|
|
df_from_start = df[df['date'] >= start_dt_obj].copy()
|
|
|
|
if df_from_start.empty:
|
|
return []
|
|
|
|
# 3. FREQUENCY LOGIC (Hardened)
|
|
# Standardize string for comparison
|
|
freq_check = str(frequency).strip().title()
|
|
|
|
if freq_check == "Weekly":
|
|
final_df = df_from_start.iloc[::5].copy()
|
|
step_increment = float((monthly_goal * 12) / 52)
|
|
elif freq_check == "Bi-Weekly":
|
|
final_df = df_from_start.iloc[::10].copy()
|
|
step_increment = float((monthly_goal * 12) / 26)
|
|
else:
|
|
# For Monthly, we need the anchor day
|
|
anchor_day = start_dt_obj.day
|
|
final_df = df_from_start[df_from_start['date'].dt.day >= anchor_day].groupby([
|
|
df_from_start['date'].dt.year,
|
|
df_from_start['date'].dt.month
|
|
], as_index=False).first()
|
|
step_increment = float(monthly_goal)
|
|
|
|
# 4. SAFETY: Ensure most recent price is included
|
|
final_df = final_df.reset_index(drop=True)
|
|
last_actual_row = df_from_start.iloc[[-1]]
|
|
|
|
if final_df.empty or last_actual_row.iloc[0]['date'] != final_df.iloc[-1]['date']:
|
|
final_df = pd.concat([final_df, last_actual_row]).drop_duplicates(subset=['date'])
|
|
|
|
final_df = final_df.sort_values('date').reset_index(drop=True)
|
|
|
|
# 5. STRATEGY INITIALIZATION
|
|
def get_shares(cash, prc):
|
|
if prc <= 0: return 0
|
|
if allow_fractional:
|
|
return float(cash / prc)
|
|
else:
|
|
# Handles both buying (+) and selling (-) for whole shares
|
|
return float(math.floor(cash / prc)) if cash >= 0 else float(math.ceil(cash / prc))
|
|
|
|
# Ensure these are initialized as floats
|
|
va_shares, dca_shares = 0.0, 0.0
|
|
va_invested, dca_invested = 0.0, 0.0
|
|
|
|
history = []
|
|
|
|
for step, (idx, row) in enumerate(final_df.iterrows()):
|
|
actual_date_str = row['date'].strftime('%Y-%m-%d')
|
|
price = float(row['close'])
|
|
|
|
if step == 0:
|
|
# First row: Both strategies start with the Initial Investment
|
|
va_actual_inv = float(initial_inv)
|
|
dca_actual_inv = float(initial_inv)
|
|
va_target_value = float(initial_inv)
|
|
else:
|
|
# Subsequent rows: Use the frequency-adjusted step_increment
|
|
va_target_value += step_increment
|
|
|
|
# DCA logic: Always invests the same amount every period
|
|
dca_actual_inv = float(step_increment)
|
|
|
|
# VA logic: Invests enough to hit the target value
|
|
current_va_market_val = va_shares * price
|
|
diff = va_target_value - current_va_market_val
|
|
|
|
# Apply "Allow Sell" constraint
|
|
va_actual_inv = diff if (diff >= 0 or allow_sell) else 0.0
|
|
|
|
# Update Shares based on fractional setting
|
|
va_new_shares = get_shares(va_actual_inv, price)
|
|
dca_new_shares = get_shares(dca_actual_inv, price)
|
|
|
|
# Running totals for shares
|
|
va_shares += va_new_shares
|
|
dca_shares += dca_new_shares
|
|
|
|
# Running totals for principal invested
|
|
va_invested += va_actual_inv
|
|
dca_invested += dca_actual_inv
|
|
|
|
history.append({
|
|
"date": actual_date_str,
|
|
"price": round(price, 2),
|
|
"va_diff": round(va_actual_inv, 2), # Invested this step (VA)
|
|
"va_shares_trans": round(va_new_shares, 4),
|
|
"va_value": round(va_shares * price, 2), # Current Portfolio Value (VA)
|
|
"va_invested": round(va_invested, 2), # Total Out-of-Pocket (VA)
|
|
"va_shares_total": round(va_shares, 4),
|
|
"va_target_value": round(float(va_target_value), 2),
|
|
"dca_diff": round(dca_actual_inv, 2), # Invested this step (DCA)
|
|
"dca_shares_trans": round(dca_new_shares, 4),
|
|
"dca_value": round(dca_shares * price, 2), # Current Portfolio Value (DCA)
|
|
"dca_invested": round(dca_invested, 2), # Total Out-of-Pocket (DCA)
|
|
"dca_shares_total": round(dca_shares, 4)
|
|
})
|
|
|
|
return history |