diff --git a/__pycache__/engine.cpython-313.pyc b/__pycache__/engine.cpython-313.pyc
index 8ea9256..4c0f052 100644
Binary files a/__pycache__/engine.cpython-313.pyc and b/__pycache__/engine.cpython-313.pyc differ
diff --git a/app.py b/app.py
index 5e0f7d0..0526d11 100644
--- a/app.py
+++ b/app.py
@@ -4,6 +4,7 @@ from engine import DataEngine
import concurrent.futures
from flask_caching import Cache
import csv, os, logging
+from concurrent.futures import ThreadPoolExecutor
app = Flask(__name__)
cache = Cache(app, config={'CACHE_TYPE': 'SimpleCache'})
@@ -11,52 +12,6 @@ cache = Cache(app, config={'CACHE_TYPE': 'SimpleCache'})
import os
import csv
-def load_instruments_from_csv(file_path):
- instruments = []
-
- # Standard static templates
- # For AGI, we use the ISIN-based Tearsheet URL
- TEMPLATES = {
- 'jpm': "https://am.jpmorgan.com/FundsMarketingHandler/historicalData?cusip={cusip}&country=hk&role=per",
- 'yahoo': "https://query1.finance.yahoo.com/v8/finance/chart/{cusip}?range=5y&interval=1d",
- 'agi': "https://markets.ft.com/data/funds/tearsheet/historical?s={cusip}"
- }
-
- try:
- abs_path = os.path.join(os.path.dirname(__file__), file_path)
-
- if not os.path.exists(abs_path):
- print(f"Error: {file_path} not found.")
- return []
-
- with open(abs_path, mode='r', encoding='utf-8-sig') as csvfile:
- reader = csv.DictReader(csvfile)
- # Standardize header names to lowercase
- reader.fieldnames = [name.strip().lower() for name in reader.fieldnames]
-
- for row in reader:
- symbol = row.get('symbol', '').strip()
- cusip = row.get('cusip', '').strip()
- provider = row.get('provider', 'jpm').strip().lower()
-
- if symbol and cusip:
- # Fetch correct template; default to JPM if provider is unknown
- template = TEMPLATES.get(provider, TEMPLATES['jpm'])
- url = template.format(cusip=cusip)
-
- instruments.append({
- "symbol": symbol,
- "url": url,
- "provider": provider
- })
-
- except Exception as e:
- print(f"CSV Loading Error: {e}")
-
- return instruments
-
-# Usage
-URL_CONFIG = load_instruments_from_csv('instruments.csv')
@cache.memoize(timeout=3600)
def fetch_and_calculate(config):
@@ -75,13 +30,38 @@ def index():
@app.route('/api/summary')
def get_summary():
+ engine_base = DataEngine()
+ instruments = engine_base.load_instruments_from_csv('instruments.csv')
+
results = []
- with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
- futures = [executor.submit(fetch_and_calculate, cfg) for cfg in URL_CONFIG]
- for f in concurrent.futures.as_completed(futures):
- if f.result(): results.append(f.result())
- results.sort(key=lambda x: x['symbol'])
+ for item in instruments:
+ engine = DataEngine(
+ symbol=item['symbol'],
+ url=item['url'],
+ provider=item['provider']
+ )
+
+ metrics = engine.get_local_metrics()
+
+ if metrics and isinstance(metrics, dict):
+ metrics['symbol'] = item['symbol']
+ results.append(metrics)
+ else:
+ # š„ FIX: Include the 'error' key so JavaScript hits the Gatekeeper
+ results.append({
+ "symbol": item['symbol'],
+ "last_close": None, # Ensure this is null, not the string "No Data"
+ "error": True
+ })
+
return jsonify(results)
+@app.route('/api/sync', methods=['POST'])
+def run_sync():
+ # ā
THIS RUNS THE FULL fetch_data() WITH NETWORK ACCESS
+ engine = DataEngine()
+ report = engine.global_sync()
+ return jsonify(report)
+
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=5000)
\ No newline at end of file
diff --git a/data_cache/SPMO ETF - USD.csv b/data_cache/SPMO ETF - USD.csv
index ab8392c..519e232 100644
--- a/data_cache/SPMO ETF - USD.csv
+++ b/data_cache/SPMO ETF - USD.csv
@@ -1,4 +1,1335 @@
date,close
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@@ -1254,26 +2585,4 @@ date,close
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+2026-01-26 21:00:00,118.83000183105469
diff --git a/engine.py b/engine.py
index cc183ac..28217e0 100644
--- a/engine.py
+++ b/engine.py
@@ -1,48 +1,179 @@
import pandas as pd
import requests
import os
-from datetime import datetime
+import shutil
+from datetime import datetime, time
from ta.trend import EMAIndicator
from ta.momentum import StochasticOscillator
class DataEngine:
- def __init__(self, symbol, url, provider):
+ def __init__(self, symbol=None, url=None, provider=None, data_dir='data_cache'):
self.symbol = symbol
self.url = url
self.provider = provider
- # 1. Get the folder where engine.py lives
+ # Use your robust path logic
base_path = os.path.dirname(os.path.abspath(__file__))
-
- # 2. Define the cache directory path
- self.cache_dir = os.path.join(base_path, "data_cache")
-
- # 3. Create the folder if it doesn't exist (safety-first)
+ self.cache_dir = os.path.join(base_path, data_dir) # Use data_dir variable
os.makedirs(self.cache_dir, exist_ok=True)
- # 4. Set the full path for this specific instrument's CSV
- self.file_path = os.path.join(self.cache_dir, f"{self.symbol}.csv")
+ # 4. Only set file_path if we actually have a symbol
+ if self.symbol:
+ self.file_path = os.path.join(self.cache_dir, f"{self.symbol}.csv")
+ else:
+ self.file_path = None
- def global_sync(self):
- """The 'One-Click' background loop."""
- # 1. Get the latest list of instruments from your CSV
- all_instruments = self.load_instruments_from_csv()
+ def load_instruments_from_csv(self, file_path):
+ import csv
+ instruments = []
- for item in all_instruments:
- # 2. Update the 'Current' target for the engine
- self.symbol = item['symbol']
- self.cusip = item['cusip']
- self.provider = item['provider']
+ # Updated templates for maximum historical reach
+ TEMPLATES = {
+ 'jpm': "https://am.jpmorgan.com/FundsMarketingHandler/historicalData?cusip={cusip}&country=hk&role=per",
+ # period1=0 fetches from the earliest available date; interval=1d is daily
+ 'yahoo': "https://query1.finance.yahoo.com/v8/finance/chart/{cusip}?period1=0&period2=9999999999&interval=1d&events=history",
+ # FT remains 30-day window; Smart Append logic in fetch_data handles the history
+ 'agi': "https://markets.ft.com/data/funds/tearsheet/historical?s={cusip}"
+ }
+
+ try:
+ abs_path = os.path.join(os.path.dirname(__file__), file_path)
- # 3. Regenerate the URL and File Path for THIS specific instrument
- self.url = self.generate_url()
- self.file_path = os.path.join(self.data_dir, f"{self.symbol}.csv")
-
- # 4. Run the robust fetch/merge logic we built
- print(f"Syncing {self.symbol}...")
- self.fetch_data()
-
- print("Global Sync Complete.")
+ if not os.path.exists(abs_path):
+ print(f"Error: {file_path} not found.")
+ return []
+
+ with open(abs_path, mode='r', encoding='utf-8-sig') as csvfile:
+ reader = csv.DictReader(csvfile)
+ reader.fieldnames = [name.strip().lower() for name in reader.fieldnames]
+
+ for row in reader:
+ symbol = row.get('symbol', '').strip()
+ cusip = row.get('cusip', '').strip()
+ provider = row.get('provider', 'jpm').strip().lower()
+
+ if symbol and cusip:
+ template = TEMPLATES.get(provider, TEMPLATES['jpm'])
+ url = template.format(cusip=cusip)
+
+ instruments.append({
+ "symbol": symbol,
+ "url": url,
+ "provider": provider,
+ "cusip": cusip # Added this so sync_all can use it if needed
+ })
+
+ except Exception as e:
+ print(f"CSV Loading Error: {e}")
+ return instruments
+ # URL_CONFIG = load_instruments_from_csv('instruments.csv')
+
+ def global_sync(self):
+ """Backup, Sync all instruments, and return a summary report."""
+ # 1. Run Maintenance/Backup
+ self.run_pre_sync_maintenance()
+
+ # FIX 1: Add 'self.' so it calls the method inside this class
+ instruments = self.load_instruments_from_csv('instruments.csv')
+
+ report = {
+ "total": len(instruments),
+ "updated": 0,
+ "failed": 0,
+ "details": []
+ }
+
+ for item in instruments:
+ try:
+ self.symbol = item['symbol']
+ self.provider = item['provider']
+ self.url = item['url']
+
+ # FIX 2: Use 'self.cache_dir' to match your __init__ logic
+ self.file_path = os.path.join(self.cache_dir, f"{self.symbol}.csv")
+
+ print(f"Updating {self.symbol}...")
+
+ # fetch_data now returns the updated DataFrame or None
+ result_df = self.fetch_data()
+
+ time.sleep(1)
+
+ if result_df is not None and not result_df.empty:
+ report["updated"] += 1
+ last_price = result_df['close'].iloc[-1]
+ report["details"].append(f"ā
{self.symbol}: Updated (Price: {last_price})")
+ else:
+ report["failed"] += 1
+ report["details"].append(f"ā {self.symbol}: No new data found")
+
+ except Exception as e:
+ report["failed"] += 1
+ report["details"].append(f"ā ļø {self.symbol}: Error ({str(e)})")
+
+ return report
+
+ def run_pre_sync_maintenance(self):
+ """Backs up files and reports current data health."""
+ import os
+ import shutil
+ import pandas as pd
+ from datetime import datetime
+
+ # 1. Setup paths correctly
+ base_dir = os.path.dirname(os.path.abspath(__file__))
+ backup_dir = os.path.join(base_dir, 'backups')
+
+ # 2. Create the timestamped folder path FIRST
+ timestamp = datetime.now().strftime("%Y%m%d_%H%M")
+ current_backup_path = os.path.join(backup_dir, f"sync_backup_{timestamp}")
+
+ # 3. Create the directories (safety-first)
+ os.makedirs(current_backup_path, exist_ok=True)
+
+ print(f"\n--- Pre-Sync Health Check ({timestamp}) ---")
+ stats = []
+
+ # 4. Check if cache exists to avoid errors
+ if not os.path.exists(self.cache_dir):
+ print(f"ā ļø Cache directory not found at {self.cache_dir}")
+ return pd.DataFrame()
+
+ # 5. Backup loop
+ for filename in os.listdir(self.cache_dir):
+ if filename.endswith(".csv"):
+ src = os.path.join(self.cache_dir, filename)
+ dst = os.path.join(current_backup_path, filename)
+
+ try:
+ # Perform copy
+ shutil.copy2(src, dst)
+
+ # Read data for health check
+ df = pd.read_csv(src)
+
+ # Store stats
+ stats.append({
+ "Fund": filename.replace(".csv", ""),
+ "Rows": len(df),
+ "Start": df['date'].min() if 'date' in df.columns else "N/A",
+ "End": df['date'].max() if 'date' in df.columns else "N/A"
+ })
+ print(f"š¦ Backed up: {filename} ({len(df)} rows)")
+
+ except Exception as e:
+ print(f"ā ļø Could not backup {filename}: {e}")
+ continue
+
+ # 6. Display and return report
+ if stats:
+ stats_df = pd.DataFrame(stats)
+ print("\n" + stats_df.to_string(index=False))
+ print(f"\nā
All backups saved to: {current_backup_path}")
+ return stats_df
+ else:
+ print("š No CSV files found to backup.")
+ return pd.DataFrame()
def _parse_jpm(self, json_data):
if isinstance(json_data, dict) and "historicalNAVList" in json_data:
@@ -165,6 +296,24 @@ class DataEngine:
print(f"Network error for {self.symbol}: {e}")
return local_df
+ def get_local_metrics(self):
+ """Reads ONLY from local CSV and returns metrics immediately."""
+ if not os.path.exists(self.file_path):
+ return {"error": "Missing Local Data", "status": "needs_sync"}
+
+ try:
+ df = pd.read_csv(self.file_path)
+ # Ensure columns are clean
+ df.columns = [c.lower().strip() for c in df.columns]
+ df['date'] = pd.to_datetime(df['date'], errors='coerce')
+ df = df.dropna(subset=['date', 'close']).sort_values('date')
+
+ # Pass this local dataframe to your existing calculation function
+ return self.calculate_table_metrics(df)
+ except Exception as e:
+ print(f"Error reading local data for {self.symbol}: {e}")
+ return None
+
def calculate_table_metrics(self, df):
if df is None or df.empty or len(df) < 2:
diff --git a/templates/index.html b/templates/index.html
index 5dfe590..22fbb49 100644
--- a/templates/index.html
+++ b/templates/index.html
@@ -5,6 +5,7 @@
Financial Dashboard
+
@@ -104,9 +120,16 @@