update cron job
This commit is contained in:
MuslemRahimi
parent
8a12c019e0
commit
7470cee293
@ -1,31 +1,124 @@
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import orjson
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import asyncio
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import aiohttp
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import aiofiles
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import sqlite3
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from datetime import datetime
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from ml_models.fundamental_predictor import FundamentalPredictor
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import yfinance as yf
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from collections import defaultdict
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import pandas as pd
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from tqdm import tqdm
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import concurrent.futures
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import re
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from itertools import combinations
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from ta.momentum import *
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from ta.trend import *
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from ta.volatility import *
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from ta.volume import *
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import gc
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#Enable automatic garbage collection
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gc.enable()
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import yfinance as yf
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import numpy as np
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score, classification_report
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.model_selection import train_test_split
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import pickle
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from datetime import datetime
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import asyncio
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import time
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async def save_json(symbol, data):
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with open(f"json/fundamental-predictor-analysis/{symbol}.json", 'w') as file:
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file.write(orjson.dumps(data))
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class TrendPredictor:
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def __init__(self, nth_day, path="ml_models/weights/ai_score"):
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self.model = RandomForestClassifier(n_estimators=1000, max_depth=500, min_samples_split=500, random_state=42, n_jobs=-1)
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self.scaler = MinMaxScaler()
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self.nth_day = nth_day
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self.path = path
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self.model_loaded = False
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def generate_features(self, df):
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new_predictors = []
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async def download_data(ticker, con, start_date, end_date):
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try:
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# Define paths to the statement files
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statements = [
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f"json/financial-statements/ratios/quarter/{ticker}.json",
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f"json/financial-statements/cash-flow-statement/quarter/{ticker}.json",
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f"json/financial-statements/income-statement/quarter/{ticker}.json",
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f"json/financial-statements/balance-sheet-statement/quarter/{ticker}.json",
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f"json/financial-statements/income-statement-growth/quarter/{ticker}.json",
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f"json/financial-statements/balance-sheet-statement-growth/quarter/{ticker}.json",
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f"json/financial-statements/cash-flow-statement-growth/quarter/{ticker}.json",
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f"json/financial-statements/key-metrics/quarter/{ticker}.json",
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f"json/financial-statements/owner-earnings/quarter/{ticker}.json",
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]
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# Helper function to load JSON data asynchronously
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async def load_json_from_file(path):
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async with aiofiles.open(path, 'r') as f:
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content = await f.read()
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return orjson.loads(content)
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# Helper function to filter data based on keys and year
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async def filter_data(data, ignore_keys, year_threshold=2000):
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return [{k: v for k, v in item.items() if k not in ignore_keys} for item in data if int(item["date"][:4]) >= year_threshold]
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# Define keys to ignore
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ignore_keys = ["symbol", "reportedCurrency", "calendarYear", "fillingDate", "acceptedDate", "period", "cik", "link", "finalLink","pbRatio","ptbRatio"]
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# Load and filter data for each statement type
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ratios = await load_json_from_file(statements[0])
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ratios = await filter_data(ratios, ignore_keys)
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cashflow = await load_json_from_file(statements[1])
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cashflow = await filter_data(cashflow, ignore_keys)
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income = await load_json_from_file(statements[2])
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income = await filter_data(income, ignore_keys)
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balance = await load_json_from_file(statements[3])
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balance = await filter_data(balance, ignore_keys)
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income_growth = await load_json_from_file(statements[4])
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income_growth = await filter_data(income_growth, ignore_keys)
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balance_growth = await load_json_from_file(statements[5])
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balance_growth = await filter_data(balance_growth, ignore_keys)
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cashflow_growth = await load_json_from_file(statements[6])
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cashflow_growth = await filter_data(cashflow_growth, ignore_keys)
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key_metrics = await load_json_from_file(statements[7])
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key_metrics = await filter_data(key_metrics, ignore_keys)
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owner_earnings = await load_json_from_file(statements[8])
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owner_earnings = await filter_data(owner_earnings, ignore_keys)
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# Combine all the data
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combined_data = defaultdict(dict)
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# Merge the data based on 'date'
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for entries in zip(income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios, key_metrics, owner_earnings):
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for entry in entries:
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date = entry['date']
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for key, value in entry.items():
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if key not in combined_data[date]:
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combined_data[date][key] = value
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combined_data = list(combined_data.values())
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#Generate more features
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#combined_data = calculate_combinations(combined_data)
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# Download historical stock data using yfinance
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df = yf.download(ticker, start=start_date, end=end_date, interval="1d").reset_index()
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df = df.rename(columns={'Adj Close': 'close', 'Date': 'date', 'Open': 'open', 'High': 'high', 'Low': 'low', 'Volume': 'volume'})
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df['date'] = df['date'].dt.strftime('%Y-%m-%d')
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df['sma_50'] = df['close'].rolling(window=50).mean()
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df['sma_200'] = df['close'].rolling(window=200).mean()
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df['golden_cross'] = ((df['sma_50'] > df['sma_200']) & (df['sma_50'].shift(1) <= df['sma_200'].shift(1))).astype(int)
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df['volatility'] = df['close'].rolling(window=30).std()
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df['daily_return'] = df['close'].pct_change()
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df['cumulative_return'] = (1 + df['daily_return']).cumprod() - 1
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df['volume_change'] = df['volume'].pct_change()
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df['roc'] = df['close'].pct_change(periods=30) * 100 # 12-day ROC
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df['avg_volume_30d'] = df['volume'].rolling(window=30).mean()
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df['drawdown'] = df['close'] / df['close'].rolling(window=252).max() - 1
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df['macd'] = macd(df['close'])
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df['macd_signal'] = macd_signal(df['close'])
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@ -39,11 +132,13 @@ class TrendPredictor:
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df['nvi'] = NegativeVolumeIndexIndicator(close=df['close'], volume=df['volume']).negative_volume_index()
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df['obv'] = OnBalanceVolumeIndicator(close=df['close'], volume=df['volume']).on_balance_volume()
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df['vpt'] = VolumePriceTrendIndicator(close=df['close'], volume=df['volume']).volume_price_trend()
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df['rsi'] = rsi(df["close"], window=14)
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df['stoch_rsi'] = stochrsi_k(df['close'], window=14, smooth1=3, smooth2=3)
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df['bb_hband'] = bollinger_hband(df['close'], window=14)/df['close']
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df['bb_lband'] = bollinger_lband(df['close'], window=14)/df['close']
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df['rsi'] = rsi(df["close"], window=30)
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df['rolling_rsi'] = df['rsi'].rolling(window=10).mean()
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df['stoch_rsi'] = stochrsi_k(df['close'], window=30, smooth1=3, smooth2=3)
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df['rolling_stoch_rsi'] = df['stoch_rsi'].rolling(window=10).mean()
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df['bb_hband'] = bollinger_hband(df['close'], window=30)/df['close']
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df['bb_lband'] = bollinger_lband(df['close'], window=30)/df['close']
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df['adi'] = acc_dist_index(high=df['high'],low=df['low'],close=df['close'],volume=df['volume'])
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df['cmf'] = chaikin_money_flow(high=df['high'],low=df['low'],close=df['close'],volume=df['volume'], window=20)
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@ -53,119 +148,164 @@ class TrendPredictor:
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df['williams'] = WilliamsRIndicator(high=df['high'], low=df['low'], close=df['close']).williams_r()
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df['stoch'] = stoch(df['high'], df['low'], df['close'], window=14)
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df['stoch'] = stoch(df['high'], df['low'], df['close'], window=30)
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new_predictors+=['williams','fi','emv','cmf','adi','bb_hband','bb_lband','vpt','stoch','stoch_rsi','rsi','nvi','obv','macd','macd_signal','macd_hist','adx','adx_pos','adx_neg','cci','mfi']
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return new_predictors
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ta_indicators = [
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'rsi', 'macd', 'macd_signal', 'macd_hist', 'adx', 'adx_pos', 'adx_neg',
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'cci', 'mfi', 'nvi', 'obv', 'vpt', 'stoch_rsi', 'bb_hband', 'bb_lband',
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'adi', 'cmf', 'emv', 'fi', 'williams', 'stoch','sma_50','sma_200','golden_cross',
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'volatility','daily_return','cumulative_return', 'roc','avg_volume_30d',
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'rolling_rsi','rolling_stoch_rsi'
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]
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def train_model(self, X_train, y_train):
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X_train = np.where(np.isinf(X_train), np.nan, X_train)
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X_train = np.nan_to_num(X_train)
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X_train = self.scaler.fit_transform(X_train)
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# Match each combined data entry with the closest available stock price in df
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for item in combined_data:
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target_date = item['date']
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counter = 0
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max_attempts = 10
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# Look for the closest matching date in the stock data
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while target_date not in df['date'].values and counter < max_attempts:
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target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
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counter += 1
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# If max attempts are reached and no matching date is found, skip the entry
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if counter == max_attempts:
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continue
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# Find the close price for the matching date
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close_price = round(df[df['date'] == target_date]['close'].values[0], 2)
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item['price'] = close_price
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# Dynamically add all indicator values to the combined_data entry
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for indicator in ta_indicators:
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indicator_value = df[df['date'] == target_date][indicator].values[0]
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item[indicator] = indicator_value # Add the indicator value to the combined_data entry
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# Sort the combined data by date
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combined_data = sorted(combined_data, key=lambda x: x['date'])
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# Convert combined data into a DataFrame
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df_combined = pd.DataFrame(combined_data).dropna()
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key_elements = [
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'revenue',
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'costOfRevenue',
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'grossProfit',
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'netIncome',
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'operatingIncome',
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'operatingExpenses',
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'researchAndDevelopmentExpenses',
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'ebitda',
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'freeCashFlow',
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'incomeBeforeTax',
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'incomeTaxExpense',
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'epsdiluted',
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'debtRepayment',
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'dividendsPaid',
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'depreciationAndAmortization',
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'netCashUsedProvidedByFinancingActivities',
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'changeInWorkingCapital',
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'stockBasedCompensation',
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'deferredIncomeTax',
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'commonStockRepurchased',
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'operatingCashFlow',
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'capitalExpenditure',
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'accountsReceivables',
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'purchasesOfInvestments',
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'cashAndCashEquivalents',
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'shortTermInvestments',
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'cashAndShortTermInvestments',
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'longTermInvestments',
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'otherCurrentLiabilities',
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'totalCurrentLiabilities',
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'longTermDebt',
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'totalDebt',
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'netDebt',
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'commonStock',
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'totalEquity',
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'totalLiabilitiesAndStockholdersEquity',
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'totalStockholdersEquity',
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'totalInvestments',
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'taxAssets',
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'totalAssets',
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'inventory',
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'propertyPlantEquipmentNet',
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'ownersEarnings',
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'averagePPE'
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]
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# Compute ratios for all combinations of key elements
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for num, denom in combinations(key_elements, 2):
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# Compute ratio num/denom
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column_name = f'{num}_to_{denom}'
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try:
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df_combined[column_name] = df_combined[num] / df_combined[denom]
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except:
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df_combined[column_name] = 0
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# Compute reverse ratio denom/num
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reverse_column_name = f'{denom}_to_{num}'
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try:
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df_combined[reverse_column_name] = df_combined[denom] / df_combined[num]
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except:
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df_combined[reverse_column_name] = 0
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# Create 'Target' column based on price change
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df_combined['Target'] = ((df_combined['price'].shift(-1) - df_combined['price']) / df_combined['price'] > 0).astype(int)
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# Return a copy of the combined DataFrame
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df_copy = df_combined.copy()
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#print(df_copy[['date','revenue','ownersEarnings','revenuePerShare']])
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return df_copy
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except Exception as e:
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print(e)
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pass
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async def process_symbol(ticker, con, start_date, end_date):
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try:
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test_size = 0.4
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start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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predictor = FundamentalPredictor()
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df = await download_data(ticker, con, start_date, end_date)
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split_size = int(len(df) * (1-test_size))
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test_data = df.iloc[split_size:]
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best_features = [col for col in df.columns if col not in ['date','price','Target']]
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data, prediction_list = predictor.evaluate_model(test_data[best_features], test_data['Target'])
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# Train model
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self.model.fit(X_train, y_train)
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with open(f'{self.path}/weights.pkl', 'wb') as f:
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pickle.dump(self.model, f, protocol=pickle.HIGHEST_PROTOCOL)
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def load_model(self):
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if not self.model_loaded:
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with open(f'{self.path}/weights.pkl', 'rb') as f:
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self.model = pickle.load(f)
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self.model_loaded = True
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'''
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output_list = [{'date': date, 'price': price, 'prediction': prediction, 'target': target}
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for (date, price,target), prediction in zip(test_data[['date', 'price','Target']].iloc[-6:].values, prediction_list[-6:])]
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'''
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#print(output_list)
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def alpha_to_score(self, alpha):
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# Convert alpha (Target) to AI Score
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if alpha <= -20:
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return 1 # Very Low Alpha
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elif -20 < alpha <= -10:
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return 2 # Low Alpha
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elif -10 < alpha <= -5:
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return 3 # Low Alpha
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elif -5 < alpha <= 0:
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return 4 # Medium Alpha
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elif 0 < alpha <= 2:
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return 5 # Medium Alpha
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elif 2 < alpha <= 4:
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return 6 # High Alpha
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elif 4 < alpha <= 6:
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return 7 # High Alpha
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elif 6 < alpha <= 8:
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return 8 # High Alpha
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elif 8 < alpha <= 10:
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return 9 # High Alpha
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elif 10 < alpha:
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return 10 # Very High Alpha
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else:
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return None
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def predict_and_score(self, df):
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self.load_model() # Ensure model is loaded once
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latest_data = df.iloc[-1].values.reshape(1, -1)
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latest_data = self.scaler.fit_transform(latest_data)
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# Predict the class (AI score)
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prediction = self.model.predict(latest_data)[0]
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# Return structured result with ticker information and score
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print(f"Predicted AI Score: {prediction}")
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return prediction
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def evaluate_model(self, X_test, y_test):
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self.load_model()
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X_test = np.where(np.isinf(X_test), np.nan, X_test)
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X_test = np.nan_to_num(X_test)
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X_test = self.scaler.transform(X_test)
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predictions = self.model.predict(X_test)
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accuracy = accuracy_score(y_test, predictions)
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print(f"Accuracy: {accuracy}")
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print("Classification Report:")
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print(classification_report(y_test, predictions))
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return accuracy
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async def download_data(ticker, start_date, end_date, spy_df, nth_day):
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df = yf.download(ticker, start=start_date, end=end_date, interval="1d")
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df = df.rename(columns={'Adj Close': 'close', 'Open': 'open', 'High': 'high', 'Low': 'low', 'Volume': 'volume'})
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df = df.reindex(spy_df.index)
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df['spy_close'] = spy_df['spy_close']
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df['stock_return'] = df['close'].pct_change()
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df['spy_return'] = df['spy_close'].pct_change()
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df['excess_return'] = df['stock_return'] - df['spy_return']
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df["Target"] = df['excess_return'].rolling(window=nth_day).sum().shift(-nth_day)*100
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# Convert the continuous Target (alpha) to a score (class)
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df["Target"] = df["Target"].apply(lambda x: TrendPredictor.alpha_to_score(self=None, alpha=x))
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return df
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if len(data) != 0:
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if data['precision'] >= 50 and data['accuracy'] >= 50:
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await save_json(ticker, data)
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except Exception as e:
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print(e)
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async def train_process(nth_day):
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tickers = ['KO','WMT','BA','PLD','AZN','LLY','INFN','GRMN','VVX','EPD','PII','WY','BLMN','AAP','ON','TGT','SMG','EL','EOG','ULTA','DV','PLNT','GLOB','LKQ','CWH','PSX','SO','TGT','GD','MU','NKE','AMGN','BX','CAT','PEP','LIN','ABBV','COST','MRK','HD','JNJ','PG','SPCB','CVX','SHEL','MS','GS','MA','V','JPM','XLF','DPZ','CMG','MCD','ALTM','PDD','MNST','SBUX','AMAT','ZS','IBM','SMCI','ORCL','XLK','VUG','VTI','VOO','IWM','IEFA','PEP','WMT','XOM','V','AVGO','BIDU','GOOGL','SNAP','DASH','SPOT','NVO','META','MSFT','ADBE','DIA','PFE','BAC','RIVN','NIO','CISS','INTC','AAPL','BYND','MSFT','HOOD','MARA','SHOP','CRM','PYPL','UBER','SAVE','QQQ','IVV','SPY','EVOK','GME','F','NVDA','AMD','AMZN','TSM','TSLA']
|
||||
#Train mode
|
||||
async def train_process(tickers, con):
|
||||
tickers = list(set(tickers))
|
||||
#print(len(tickers))
|
||||
|
||||
df_train = pd.DataFrame()
|
||||
df_test = pd.DataFrame()
|
||||
best_features = ['close','williams','fi','emv','adi','cmf','bb_hband','bb_lband','vpt','stoch','stoch_rsi','rsi','nvi','macd','mfi','cci','obv','adx','adx_pos','adx_neg']
|
||||
test_size = 0.1
|
||||
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
|
||||
test_size = 0.2
|
||||
start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
|
||||
end_date = datetime.today().strftime("%Y-%m-%d")
|
||||
predictor = TrendPredictor(nth_day=nth_day)
|
||||
|
||||
spy_df = yf.download("SPY", start=start_date, end=end_date, interval="1d")
|
||||
spy_df = spy_df.rename(columns={'Adj Close': 'spy_close'})
|
||||
df_train = pd.DataFrame()
|
||||
df_test = pd.DataFrame()
|
||||
|
||||
tasks = [download_data(ticker, start_date, end_date, spy_df, nth_day) for ticker in tickers]
|
||||
tasks = [download_data(ticker, con, start_date, end_date) for ticker in tickers]
|
||||
dfs = await asyncio.gather(*tasks)
|
||||
for df in dfs:
|
||||
try:
|
||||
predictors = predictor.generate_features(df)
|
||||
predictors = [pred for pred in predictors if pred in df.columns]
|
||||
df = df.dropna(subset=df.columns[df.columns != "nth_day"])
|
||||
split_size = int(len(df) * (1-test_size))
|
||||
train_data = df.iloc[:split_size]
|
||||
test_data = df.iloc[split_size:]
|
||||
@ -174,42 +314,73 @@ async def train_process(nth_day):
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
best_features = [col for col in df_train.columns if col not in ['date','price','Target']]
|
||||
|
||||
df_train = df_train.sample(frac=1).reset_index(drop=True)
|
||||
print('======Train Set Datapoints======')
|
||||
print(len(df_train))
|
||||
print(df_train)
|
||||
print(df_test)
|
||||
#selected_features = predictor.feature_selection(df_train[best_features], df_train['Target'],k=10)
|
||||
#print(selected_features)
|
||||
selected_features = [col for col in df_train if col not in ['price','date','Target']]
|
||||
|
||||
predictor.train_model(df_train[best_features], df_train['Target'])
|
||||
predictor.evaluate_model(df_test[best_features], df_test['Target'])
|
||||
predictor = FundamentalPredictor()
|
||||
predictor.train_model(df_train[selected_features], df_train['Target'])
|
||||
predictor.evaluate_model(df_test[selected_features], df_test['Target'])
|
||||
|
||||
async def test_process(nth_day):
|
||||
best_features = ['close','williams','fi','emv','adi','cmf','bb_hband','bb_lband','vpt','stoch','stoch_rsi','rsi','nvi','macd','mfi','cci','obv','adx','adx_pos','adx_neg']
|
||||
test_size = 0.1
|
||||
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
|
||||
async def test_process(con):
|
||||
test_size = 0.2
|
||||
start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
|
||||
end_date = datetime.today().strftime("%Y-%m-%d")
|
||||
predictor = TrendPredictor(nth_day=nth_day)
|
||||
|
||||
spy_df = yf.download("SPY", start=start_date, end=end_date, interval="1d")
|
||||
spy_df = spy_df.rename(columns={'Adj Close': 'spy_close'})
|
||||
|
||||
df = await download_data('AAPL', start_date, end_date, spy_df, nth_day)
|
||||
predictors = predictor.generate_features(df)
|
||||
|
||||
#save it to get the latest date with the latest row otherwise it drops it since of NaN for Target
|
||||
df_copy = df.copy()
|
||||
|
||||
df = df.dropna(subset=df.columns[df.columns != "nth_day"])
|
||||
predictor = FundamentalPredictor()
|
||||
df = await download_data('GME', con, start_date, end_date)
|
||||
split_size = int(len(df) * (1-test_size))
|
||||
test_data = df.iloc[split_size:]
|
||||
predictor.evaluate_model(test_data[best_features], test_data['Target'])
|
||||
selected_features = [col for col in test_data if col not in ['price','date','Target']]
|
||||
predictor.evaluate_model(test_data[selected_features], test_data['Target'])
|
||||
|
||||
#Evaluate based on non-nan results of target but predict the latest date
|
||||
predictor.predict_and_score(df_copy[best_features])
|
||||
print(df_copy)
|
||||
|
||||
async def main():
|
||||
nth_day = 60 # 60 days forward prediction
|
||||
async def run():
|
||||
|
||||
await train_process(nth_day = 60)
|
||||
#await test_process(nth_day = 60)
|
||||
#Train first model
|
||||
|
||||
con = sqlite3.connect('stocks.db')
|
||||
|
||||
cursor = con.cursor()
|
||||
cursor.execute("PRAGMA journal_mode = wal")
|
||||
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 300E9")
|
||||
stock_symbols = ['AAPL'] #[row[0] for row in cursor.fetchall()]
|
||||
print('Number of Stocks')
|
||||
print(len(stock_symbols))
|
||||
await train_process(stock_symbols, con)
|
||||
|
||||
|
||||
#Prediction Steps for all stock symbols
|
||||
|
||||
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 1E9")
|
||||
stock_symbols = [row[0] for row in cursor.fetchall()]
|
||||
|
||||
total_symbols = ['GME'] #stock_symbols
|
||||
|
||||
print(f"Total tickers: {len(total_symbols)}")
|
||||
start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
|
||||
end_date = datetime.today().strftime("%Y-%m-%d")
|
||||
|
||||
chunk_size = len(total_symbols)# // 100 # Divide the list into N chunks
|
||||
chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
|
||||
for chunk in chunks:
|
||||
tasks = []
|
||||
for ticker in tqdm(chunk):
|
||||
tasks.append(process_symbol(ticker, con, start_date, end_date))
|
||||
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
con.close()
|
||||
|
||||
try:
|
||||
asyncio.run(run())
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
||||
@ -34,7 +34,7 @@ async def save_json(symbol, period, data_type, data):
|
||||
async def get_financial_statements(session, symbol, semaphore, request_counter):
|
||||
base_url = "https://financialmodelingprep.com/api/v3"
|
||||
periods = ['quarter', 'annual']
|
||||
financial_data_types = ['income-statement', 'balance-sheet-statement', 'cash-flow-statement', 'ratios']
|
||||
financial_data_types = ['key-metrics', 'income-statement', 'balance-sheet-statement', 'cash-flow-statement', 'ratios']
|
||||
growth_data_types = ['income-statement-growth', 'balance-sheet-statement-growth', 'cash-flow-statement-growth']
|
||||
|
||||
async with semaphore:
|
||||
@ -63,6 +63,19 @@ async def get_financial_statements(session, symbol, semaphore, request_counter):
|
||||
await asyncio.sleep(60) # Pause for 60 seconds
|
||||
request_counter[0] = 0 # Reset the request counter after the pause
|
||||
|
||||
# Fetch owner earnings data
|
||||
owner_earnings_url = f"https://financialmodelingprep.com/api/v4/owner_earnings?symbol={symbol}&apikey={api_key}"
|
||||
owner_earnings_data = await fetch_data(session, owner_earnings_url, symbol)
|
||||
if owner_earnings_data:
|
||||
await save_json(symbol, 'quarter', 'owner-earnings', owner_earnings_data)
|
||||
|
||||
request_counter[0] += 1 # Increment the request counter
|
||||
if request_counter[0] >= 500:
|
||||
await asyncio.sleep(60) # Pause for 60 seconds
|
||||
request_counter[0] = 0 # Reset the request counter after the pause
|
||||
|
||||
|
||||
|
||||
async def run():
|
||||
con = sqlite3.connect('stocks.db')
|
||||
cursor = con.cursor()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user