bugfixing
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MuslemRahimi
parent
00010b7ab4
commit
5dc5e30730
@ -887,7 +887,7 @@ async def get_highest_option_iv_rank():
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total_oi = stock_screener_data_dict[symbol].get('totalOI',0)
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change_oi = stock_screener_data_dict[symbol].get('changeOI',0)
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if total_oi > 1E6 and iv_rank > 0:
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if total_oi > 1E6 and iv_rank > 0 and iv_rank < 100:
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quote_data = await get_quote_data(symbol)
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# Assign price and volume, and check if they meet the penny stock criteria
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if quote_data:
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@ -966,7 +966,7 @@ async def get_highest_option_premium():
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if res_list:
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# Sort by market cap in descending order
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res_list = sorted(res_list, key=lambda x: x['totalPrem'], reverse=True)[:50]
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# Assign rank to each stock
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for rank, item in enumerate(res_list, start=1):
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item['rank'] = rank
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@ -22,7 +22,6 @@ query_template = """
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"""
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def save_json(data, symbol):
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directory_path = f"json/options-historical-data/companies"
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os.makedirs(directory_path, exist_ok=True) # Ensure the directory exists
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@ -36,29 +35,141 @@ def safe_round(value, decimals=2):
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return value
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def calculate_iv_rank_for_all(data):
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# Extract all IV values
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iv_values = [entry['iv'] for entry in data if 'iv' in entry]
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def aggregate_data_by_date(symbol):
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data_by_date = defaultdict(lambda: {
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"date": "",
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"call_volume": 0,
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"put_volume": 0,
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"call_open_interest": 0,
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"put_open_interest": 0,
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"call_premium": 0,
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"put_premium": 0,
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"iv": 0.0, # Sum of implied volatilities
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"iv_count": 0, # Count of entries for IV
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})
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# Calculate cutoff date (1 year ago)
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today = datetime.today().date()
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one_year_ago = today - timedelta(days=365)
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one_year_ago_str = one_year_ago.strftime('%Y-%m-%d')
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contract_dir = f"json/all-options-contracts/{symbol}"
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contract_list = get_contracts_from_directory(contract_dir)
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if not iv_values:
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return None # No IV data available
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# Compute highest and lowest IV
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highest_iv = max(iv_values)
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lowest_iv = min(iv_values)
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# Calculate IV Rank for each entry
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for entry in data:
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if 'iv' in entry:
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iv = entry['iv']
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if highest_iv == lowest_iv:
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entry['iv_rank'] = 100.0 # If all IVs are the same, rank is 100%
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if len(contract_list) > 0:
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for item in contract_list:
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try:
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file_path = os.path.join(contract_dir, f"{item}.json")
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with open(file_path, "r") as file:
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data = orjson.loads(file.read())
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option_type = data.get('optionType', None)
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if option_type not in ['call', 'put']:
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continue
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for entry in data.get('history', []):
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date = entry.get('date')
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# Skip entries older than one year
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if date < one_year_ago_str:
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continue
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volume = entry.get('volume', 0) or 0
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open_interest = entry.get('open_interest', 0) or 0
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total_premium = entry.get('total_premium', 0) or 0
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implied_volatility = entry.get('implied_volatility', 0) or 0
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daily_data = data_by_date[date]
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daily_data["date"] = date
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if option_type == 'call':
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daily_data["call_volume"] += int(volume)
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daily_data["call_open_interest"] += int(open_interest)
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daily_data["call_premium"] += int(total_premium)
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elif option_type == 'put':
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daily_data["put_volume"] += int(volume)
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daily_data["put_open_interest"] += int(open_interest)
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daily_data["put_premium"] += int(total_premium)
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# Aggregate IV for both calls and puts
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daily_data["iv"] += round(implied_volatility, 2)
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daily_data["iv_count"] += 1
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# Calculate put/call ratio
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try:
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daily_data["putCallRatio"] = round(daily_data["put_volume"] / daily_data["call_volume"], 2)
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except ZeroDivisionError:
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daily_data["putCallRatio"] = None
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except Exception as e:
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print(f"Error processing {item}: {e}")
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continue
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# Convert to list and calculate average IV
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data = []
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for date, daily in data_by_date.items():
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if daily['iv_count'] > 0:
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daily['iv'] = round(daily['iv'] / daily['iv_count'], 2)
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else:
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entry['iv_rank'] = round(((iv - lowest_iv) / (highest_iv - lowest_iv)) * 100,2)
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else:
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entry['iv_rank'] = None # Handle missing IV
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daily['iv'] = None
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data.append(daily)
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# Sort and calculate IV Rank
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data = sorted(data, key=lambda x: x['date'])
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data = calculate_iv_rank_for_all(data)
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data = sorted(data, key=lambda x: x['date'], reverse=True)
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return data
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return data
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else:
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return []
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def calculate_iv_rank_for_all(data):
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if not data:
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return []
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# Convert to DataFrame
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df = pd.DataFrame(data)
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# Check if 'iv' exists and filter out entries without IV
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if 'iv' not in df.columns or df['iv'].isnull().all():
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for entry in data:
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entry['iv_rank'] = None
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return data
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# Convert date to datetime and sort
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df['date'] = pd.to_datetime(df['date'])
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df.sort_values('date', inplace=True)
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# Calculate rolling 365-day min and max for IV
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df.set_index('date', inplace=True)
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rolling_min = df['iv'].rolling('365D', min_periods=1).min()
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rolling_max = df['iv'].rolling('365D', min_periods=1).max()
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# Merge back into DataFrame
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df['rolling_min'] = rolling_min
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df['rolling_max'] = rolling_max
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# Calculate IV Rank
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df['iv_rank'] = ((df['iv'] - df['rolling_min']) / (df['rolling_max'] - df['rolling_min'])) * 100
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df['iv_rank'] = df['iv_rank'].round(2)
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# Handle cases where max == min
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df.loc[df['rolling_max'] == df['rolling_min'], 'iv_rank'] = 100.0
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# Replace NaN with None
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df['iv_rank'] = df['iv_rank'].where(pd.notnull(df['iv_rank']), None)
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# Drop temporary columns
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df.drop(['rolling_min', 'rolling_max'], axis=1, inplace=True)
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# Convert back to list of dicts
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df.reset_index(inplace=True)
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df['date'] = df['date'].dt.strftime('%Y-%m-%d')
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result = df.to_dict('records')
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# Sort in reverse chronological order
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result = sorted(result, key=lambda x: x['date'], reverse=True)
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return result
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def prepare_data(data, symbol):
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@ -155,84 +266,6 @@ def get_contracts_from_directory(directory: str):
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return []
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def aggregate_data_by_date(symbol):
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data_by_date = defaultdict(lambda: {
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"date": "",
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"call_volume": 0,
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"put_volume": 0,
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"call_open_interest": 0,
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"put_open_interest": 0,
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"call_premium": 0,
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"call_net_premium": 0,
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"put_premium": 0,
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"put_net_premium": 0,
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"iv": 0, # Sum of implied volatilities
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"iv_count": 0, # Count of entries for IV
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})
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contract_dir = f"json/all-options-contracts/{symbol}"
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contract_list = get_contracts_from_directory(contract_dir)
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if len(contract_list) > 0:
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for item in contract_list:
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try:
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file_path = os.path.join(contract_dir, f"{item}.json")
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with open(file_path, "r") as file:
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data = orjson.loads(file.read())
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option_type = data.get('optionType', None)
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if option_type not in ['call', 'put']:
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continue
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for entry in data.get('history', []):
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date = entry.get('date')
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volume = entry.get('volume', 0) or 0
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open_interest = entry.get('open_interest', 0) or 0
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total_premium = entry.get('total_premium', 0) or 0
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implied_volatility = entry.get('implied_volatility', 0) or 0
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if date:
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daily_data = data_by_date[date]
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daily_data["date"] = date
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if option_type == 'call':
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daily_data["call_volume"] += int(volume)
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daily_data["call_open_interest"] += int(open_interest)
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daily_data["call_premium"] += int(total_premium)
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elif option_type == 'put':
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daily_data["put_volume"] += int(volume)
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daily_data["put_open_interest"] += int(open_interest)
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daily_data["put_premium"] += int(total_premium)
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daily_data["iv"] += round(implied_volatility, 2)
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daily_data["iv_count"] += 1
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try:
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daily_data["putCallRatio"] = round(daily_data["put_volume"] / daily_data["call_volume"], 2)
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except ZeroDivisionError:
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daily_data["putCallRatio"] = None
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except:
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pass
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# Convert to list of dictionaries and sort by date
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data = list(data_by_date.values())
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for daily_data in data:
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try:
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if daily_data["iv_count"] > 0:
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daily_data["iv"] = round(daily_data["iv"] / daily_data["iv_count"], 2)
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else:
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daily_data["iv"] = None # Or set it to 0 if you prefer
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except:
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daily_data["iv"] = None
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data = sorted(data, key=lambda x: x['date'], reverse=True)
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data = calculate_iv_rank_for_all(data)
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return data
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else:
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return []
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@ -253,7 +286,7 @@ etf_symbols = [row[0] for row in etf_cursor.fetchall()]
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total_symbols = stocks_symbols + etf_symbols
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for symbol in tqdm(total_symbols):
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for symbol in tqdm(['AAPL']):
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try:
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data = aggregate_data_by_date(symbol)
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data = prepare_data(data, symbol)
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@ -916,27 +916,21 @@ async def get_stock_screener(con):
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item['shortFloatPercent'] = None
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try:
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with open(f"json/options-stats/companies/{symbol}.json", "r") as file:
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res = orjson.loads(file.read())
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item['gexRatio'] = res['gex_ratio']
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with open(f"json/options-historical-data/companies/{symbol}.json", "r") as file:
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res = orjson.loads(file.read())[0]
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item['ivRank'] = res['iv_rank']
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item['iv30d'] = res['iv30d']
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item['iv30d'] = res['iv']
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item['totalOI'] = res['total_open_interest']
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item['changeOI'] = res['open_interest_change']
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item['netCallPrem'] = res['net_call_premium']
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item['netPutPrem'] = res['net_put_premium']
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item['changeOI'] = res['changeOI']
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item['callVolume'] = res['call_volume']
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item['putVolume'] = res['put_volume']
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item['pcRatio'] = res['put_call_ratio']
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item['totalPrem'] = res['call_premium']+res['put_premium']
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item['pcRatio'] = res['putCallRatio']
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item['totalPrem'] = res['total_premium']
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except:
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item['gexRatio'] = None
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item['ivRank'] = None
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item['iv30d'] = None
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item['totalOI'] = None
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item['changeOI'] = None
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item['netCallPrem'] = None
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item['netPutPrem'] = None
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item['callVolume'] = None
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item['putVolume'] = None
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item['pcRatio'] = None
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