add chain data
This commit is contained in:
MuslemRahimi
parent
b4bd8a545f
commit
d12b5691a5
@ -18,6 +18,20 @@ def save_json(symbol, data, file_path):
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with open(f'{file_path}/{symbol}.json', 'w') as file:
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ujson.dump(data, file)
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# Define the keys to keep
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keys_to_keep = {'time', 'sentiment', 'option_activity_type', 'price', 'underlying_price', 'cost_basis', 'strike_price', 'date', 'date_expiration', 'open_interest', 'put_call', 'volume'}
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def filter_data(item):
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# Filter the item to keep only the specified keys and format fields
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filtered_item = {key: value for key, value in item.items() if key in keys_to_keep}
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filtered_item['type'] = filtered_item['option_activity_type'].capitalize()
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filtered_item['sentiment'] = filtered_item['sentiment'].capitalize()
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filtered_item['underlying_price'] = round(float(filtered_item['underlying_price']), 2)
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filtered_item['put_call'] = 'Calls' if filtered_item['put_call'] == 'CALL' else 'Puts'
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return filtered_item
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def calculate_volatility(prices_df):
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prices_df = prices_df.sort_values(by='date')
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prices_df['return'] = prices_df['close'].pct_change()
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@ -103,7 +117,7 @@ def calculate_otm_percentage(option_data_list):
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return 0
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def summarize_option_chain_with_otm(option_data_list, df_price):
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def get_historical_option_data(option_data_list, df_price):
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summary_data = []
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for option_data in option_data_list:
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@ -207,6 +221,70 @@ def summarize_option_chain_with_otm(option_data_list, df_price):
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# Return the summarized dataframe
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return daily_summary
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def get_options_chain(option_data_list):
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# Convert raw data to DataFrame and ensure correct data types
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df = pd.DataFrame(option_data_list)
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type_conversions = {
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'cost_basis': float,
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'volume': int,
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'open_interest': int,
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'strike_price': float,
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'date_expiration': str # Ensuring date_expiration is initially a string
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}
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for col, dtype in type_conversions.items():
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df[col] = df[col].astype(dtype)
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# Convert 'date_expiration' to datetime
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df['date_expiration'] = pd.to_datetime(df['date_expiration'])
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# Filter out rows where 'date_expiration' is in the past
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current_date = datetime.now()
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df = df[df['date_expiration'] > current_date]
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# Calculate total premium during grouping
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df['total_premium'] = df['cost_basis']
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# Group and aggregate data
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grouped = df.groupby(['date_expiration', 'strike_price', 'put_call']).agg(
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total_open_interest=('open_interest', 'sum'),
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total_volume=('volume', 'sum'),
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total_premium=('total_premium', 'sum')
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).reset_index()
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# Pivot the data for puts and calls
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pivoted = grouped.pivot_table(
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index=['date_expiration', 'strike_price'],
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columns='put_call',
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values=['total_open_interest', 'total_volume', 'total_premium'],
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fill_value=0
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).reset_index()
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# Flatten column names
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pivoted.columns = [' '.join(col).strip() for col in pivoted.columns.values]
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# Rename columns for clarity
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new_column_names = {
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'total_open_interest CALL': 'total_open_interest_call',
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'total_open_interest PUT': 'total_open_interest_put',
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'total_volume CALL': 'total_volume_call',
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'total_volume PUT': 'total_volume_put',
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'total_premium CALL': 'total_premium_call',
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'total_premium PUT': 'total_premium_put'
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}
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pivoted = pivoted.rename(columns=new_column_names)
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# Convert 'date_expiration' to string in ISO format
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pivoted['date_expiration'] = pivoted['date_expiration'].dt.strftime('%Y-%m-%dT%H:%M:%S')
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# Ensure we capture all relevant columns
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columns_to_keep = ['strike_price'] + [col for col in pivoted.columns if col not in ['strike_price', 'date_expiration']]
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# Construct the options chain
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option_chain = pivoted.groupby('date_expiration').apply(
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lambda x: x[columns_to_keep].to_dict(orient='records')
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).reset_index(name='chain')
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return option_chain
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def get_data(ticker):
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res_list = []
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@ -224,19 +302,6 @@ def get_data(ticker):
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return res_list
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# Define the keys to keep
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keys_to_keep = {'time', 'sentiment', 'option_activity_type', 'price', 'underlying_price', 'cost_basis', 'strike_price', 'date', 'date_expiration', 'open_interest', 'put_call', 'volume'}
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def filter_data(item):
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# Filter the item to keep only the specified keys and format fields
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filtered_item = {key: value for key, value in item.items() if key in keys_to_keep}
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filtered_item['type'] = filtered_item['option_activity_type'].capitalize()
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filtered_item['sentiment'] = filtered_item['sentiment'].capitalize()
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filtered_item['underlying_price'] = round(float(filtered_item['underlying_price']), 2)
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filtered_item['put_call'] = 'Calls' if filtered_item['put_call'] == 'CALL' else 'Puts'
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return filtered_item
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# Define date range
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end_date = date.today()
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start_date = end_date - timedelta(180)
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@ -266,7 +331,7 @@ query_template = """
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"""
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# Process each symbol
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for ticker in ['GME']: # total_symbols
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for ticker in total_symbols:
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try:
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query = query_template.format(ticker=ticker)
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df_price = pd.read_sql_query(query, stock_con if ticker in stock_symbols else etf_con, params=(start_date_str, end_date_str)).round(2)
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@ -282,14 +347,21 @@ for ticker in ['GME']: # total_symbols
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filtered_item = filter_data(item)
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grouped_history[filtered_item['date']].append(filtered_item)
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daily_option_chain = summarize_option_chain_with_otm(ticker_data, df_price)
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daily_option_chain = daily_option_chain.merge(df_price[['date', 'changesPercentage']], on='date', how='inner')
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daily_historical_option_data = get_historical_option_data(ticker_data, df_price)
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daily_historical_option_data = daily_historical_option_data.merge(df_price[['date', 'changesPercentage']], on='date', how='inner')
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# Add "history" column containing all filtered items with the same date
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daily_option_chain['history'] = daily_option_chain['date'].apply(lambda x: grouped_history.get(x, []))
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daily_historical_option_data['history'] = daily_historical_option_data['date'].apply(lambda x: grouped_history.get(x, []))
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if not daily_historical_option_data.empty:
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save_json(ticker, daily_historical_option_data.to_dict('records'), 'json/options-historical-data/companies')
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option_chain_data = get_options_chain(ticker_data)
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if not option_chain_data.empty:
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save_json(ticker, option_chain_data.to_dict('records'), 'json/options-chain/companies')
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if not daily_option_chain.empty:
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save_json(ticker, daily_option_chain.to_dict('records'), 'json/options-chain/companies')
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daily_gex = compute_daily_gex(ticker_data, volatility)
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daily_gex = daily_gex.merge(df_price[['date', 'close']], on='date', how='inner')
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33
app/main.py
33
app/main.py
@ -2563,10 +2563,38 @@ async def get_options_flow_ticker(data:TickerData, api_key: str = Security(get_a
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headers={"Content-Encoding": "gzip"}
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)
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@app.post("/options-chain-ticker")
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@app.post("/options-historical-data-ticker")
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async def get_options_chain(data:TickerData, api_key: str = Security(get_api_key)):
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ticker = data.ticker.upper()
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cache_key = f"options-chain-{ticker}"
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cache_key = f"options-historical-data-{ticker}"
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cached_result = redis_client.get(cache_key)
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if cached_result:
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return StreamingResponse(
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io.BytesIO(cached_result),
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media_type="application/json",
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headers={"Content-Encoding": "gzip"})
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try:
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with open(f"json/options-historical-data/companies/{ticker}.json", 'rb') as file:
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res_list = orjson.loads(file.read())
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except:
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res_list = []
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data = orjson.dumps(res_list)
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compressed_data = gzip.compress(data)
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redis_client.set(cache_key, compressed_data)
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redis_client.expire(cache_key, 3600*3600) # Set cache expiration time to 5 min
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return StreamingResponse(
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io.BytesIO(compressed_data),
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media_type="application/json",
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headers={"Content-Encoding": "gzip"}
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)
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@app.post("/options-chain-data-ticker")
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async def get_options_chain(data:TickerData, api_key: str = Security(get_api_key)):
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ticker = data.ticker.upper()
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cache_key = f"options-chain-data-{ticker}"
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cached_result = redis_client.get(cache_key)
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if cached_result:
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@ -2591,7 +2619,6 @@ async def get_options_chain(data:TickerData, api_key: str = Security(get_api_key
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headers={"Content-Encoding": "gzip"}
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)
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'''
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@app.post("/options-flow-feed")
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async def get_options_flow_feed(data: LastOptionId, api_key: str = Security(get_api_key)):
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