update
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@ -75,6 +75,7 @@ async def fetch_all_data(session, symbol, time_period):
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url = f"https://financialmodelingprep.com/api/v3/historical-chart/{time_period}/{symbol}?serietype=bar&extend=false&from={current_start_date.strftime('%Y-%m-%d')}&to={current_end_date.strftime('%Y-%m-%d')}&apikey={api_key}"
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url = f"https://financialmodelingprep.com/api/v3/historical-chart/{time_period}/{symbol}?serietype=bar&extend=false&from={current_start_date.strftime('%Y-%m-%d')}&to={current_end_date.strftime('%Y-%m-%d')}&apikey={api_key}"
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data = await fetch_data(session, url)
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data = await fetch_data(session, url)
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print("api endpoint called")
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if data:
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if data:
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all_data.extend(data) # Accumulate the fetched data
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all_data.extend(data) # Accumulate the fetched data
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@ -119,7 +120,7 @@ async def run():
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# List of total symbols to process
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# List of total symbols to process
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total_symbols = stock_symbols # Use stock_symbols + etf_symbols if needed
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total_symbols = stock_symbols # Use stock_symbols + etf_symbols if needed
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chunk_size = len(total_symbols) #// 500 # Divide the list into N chunks
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chunk_size = len(total_symbols) // 500 # Divide the list into N chunks
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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for chunk in tqdm(chunks):
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for chunk in tqdm(chunks):
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