import os import pickle import numpy as np from xgboost import XGBRegressor from zipfile import ZipFile from datetime import datetime, timedelta import pandas as pd from sklearn.model_selection import train_test_split from updater import download_binance_monthly_data, download_binance_daily_data from config import data_base_path, model_file_path binance_data_path = os.path.join(data_base_path, "binance/futures-klines") training_price_data_path = os.path.join(data_base_path, "eth_price_data.csv") def download_data(): cm_or_um = "um" symbols = ["ETHUSDT"] intervals = ["10min"] years = ["2020", "2021", "2022", "2023", "2024"] months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"] download_path = binance_data_path download_binance_monthly_data( cm_or_um, symbols, intervals, years, months, download_path ) print(f"Downloaded monthly data to {download_path}.") current_datetime = datetime.now() current_year = current_datetime.year current_month = current_datetime.month download_binance_daily_data( cm_or_um, symbols, intervals, current_year, current_month, download_path ) print(f"Downloaded daily data to {download_path}.") def format_data(): files = sorted([x for x in os.listdir(binance_data_path) if x.endswith(".zip")]) if len(files) == 0: return price_df = pd.DataFrame() for file in files: zip_file_path = os.path.join(binance_data_path, file) myzip = ZipFile(zip_file_path) with myzip.open(myzip.filelist[0]) as f: line = f.readline() header = 0 if line.decode("utf-8").startswith("open_time") else None df = pd.read_csv(myzip.open(myzip.filelist[0]), header=header).iloc[:, :11] df.columns = [ "start_time", "open", "high", "low", "close", "volume", "end_time", "volume_usd", "n_trades", "taker_volume", "taker_volume_usd", ] df.index = [pd.Timestamp(x + 1, unit="ms") for x in df["end_time"]] df.index.name = "date" price_df = pd.concat([price_df, df]) price_df["timestamp"] = price_df.index.map(pd.Timestamp.timestamp) price_df["price_diff"] = price_df["close"].diff() price_df["volatility"] = (price_df["high"] - price_df["low"]) / price_df["open"] price_df["volume"] = price_df["volume"] price_df["moving_avg_7"] = price_df["close"].rolling(window=7).mean() price_df["moving_avg_30"] = price_df["close"].rolling(window=30).mean() # Удаляем строки с NaN значениями price_df.dropna(inplace=True) # Сохраняем данные price_df.sort_index().to_csv(training_price_data_path) def train_model(): price_data = pd.read_csv(training_price_data_path) # Используем дополнительные признаки x = price_data[ [ "timestamp", "price_diff", "volatility", "volume", "moving_avg_7", "moving_avg_30", ] ] y = price_data["close"] x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=0 ) # Train the model print("Training model...") model = XGBRegressor() model.fit(x_train, y_train) print("Model trained.") os.makedirs(os.path.dirname(model_file_path), exist_ok=True) with open(model_file_path, "wb") as f: pickle.dump(model, f) print(f"Trained model saved to {model_file_path}") # Optional: Оценка модели y_pred = model.predict(x_test) print(f"Mean Absolute Error: {np.mean(np.abs(y_test - y_pred))}") def get_inference_data(token, period): """ Генерирует данные для инференса на основе переданного токена и периода. """ price_data = pd.read_csv(training_price_data_path) # Настроить разницу времени в зависимости от периода time_delta_map = { "1min": timedelta(minutes=1), "5min": timedelta(minutes(5)), "10min": timedelta(minutes(10)), "30min": timedelta(minutes(30)), "1h": timedelta(hours=1), "1d": timedelta(days=1), } # Получаем последний таймстамп и вычисляем следующий last_timestamp = pd.to_datetime(price_data["timestamp"].iloc[-1], unit="s") next_timestamp = last_timestamp + time_delta_map.get(period, timedelta(minutes=10)) # Используем последние значения признаков last_data = price_data.iloc[-1] X_new = np.array( [ [ next_timestamp.timestamp(), last_data["price_diff"], last_data["volatility"], last_data["volume"], last_data["moving_avg_7"], last_data["moving_avg_30"], ] ] ) return X_new