125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
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")
|
||
|
||
|
||
def get_training_data_path(token):
|
||
"""
|
||
Возвращает путь к файлу данных для указанного токена.
|
||
"""
|
||
return os.path.join(data_base_path, f"{token}_price_data.csv")
|
||
|
||
|
||
def download_data():
|
||
cm_or_um = "um"
|
||
symbols = ["ETHUSDT", "BTCUSDT", "SOLUSDT", "BNBUSDT", "ARBUSDT"]
|
||
intervals = ["10min", "1d"]
|
||
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(token):
|
||
files = sorted(
|
||
[x for x in os.listdir(binance_data_path) if x.endswith(".zip") and token in x]
|
||
)
|
||
|
||
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
|
||
training_price_data_path = get_training_data_path(token)
|
||
price_df.sort_index().to_csv(training_price_data_path)
|
||
|
||
|
||
def train_model(token):
|
||
training_price_data_path = get_training_data_path(token)
|
||
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
|
||
)
|
||
|
||
model = XGBRegressor()
|
||
model.fit(x_train, y_train)
|
||
|
||
token_model_path = model_file_path[token]
|
||
os.makedirs(os.path.dirname(token_model_path), exist_ok=True)
|
||
|
||
with open(token_model_path, "wb") as f:
|
||
pickle.dump(model, f)
|
||
|
||
print(f"Trained model saved to {token_model_path}")
|
||
|
||
# Optional: Оценка модели
|
||
y_pred = model.predict(x_test)
|
||
print(f"Mean Absolute Error: {np.mean(np.abs(y_test - y_pred))}")
|