add many tokens
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vvzvlad
parent
2475e22c1a
commit
ca552f5a7a
30
app.py
30
app.py
@ -4,40 +4,41 @@ import pandas as pd
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import numpy as np
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from datetime import datetime
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from flask import Flask, jsonify, Response
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from model import download_data, format_data, train_model, training_price_data_path
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from model import download_data, format_data, train_model, get_training_data_path
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from config import model_file_path
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app = Flask(__name__)
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def update_data():
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"""Download price data, format data and train model."""
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"""Download price data, format data and train model for each token."""
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tokens = ["ETH", "BTC", "SOL", "BNB", "ARB"]
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download_data()
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format_data()
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train_model()
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for token in tokens:
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format_data(token)
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train_model(token)
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def get_inference(token, period):
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try:
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with open(model_file_path, "rb") as f:
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model_path = model_file_path[token]
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with open(model_path, "rb") as f:
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loaded_model = pickle.load(f)
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# Загружаем последние данные из файла
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# Загружаем последние данные для данного токена
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training_price_data_path = get_training_data_path(token)
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price_data = pd.read_csv(training_price_data_path)
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# Используем последние значения признаков для предсказания
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last_row = price_data.iloc[-1]
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# Получаем последний timestamp
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last_timestamp = last_row["timestamp"]
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# Преобразуем период в секунды (пример)
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# Преобразуем период в секунды
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period_seconds = convert_period_to_seconds(period)
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# Рассчитываем новый временной штамп на основе периода
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new_timestamp = last_timestamp + period_seconds
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# timestamp
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# Формируем данные для предсказания с новым timestamp
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X_new = np.array(
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[
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new_timestamp,
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@ -49,9 +50,8 @@ def get_inference(token, period):
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]
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).reshape(1, -1)
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# Предсказание
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# Делаем предсказание
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future_price_pred = loaded_model.predict(X_new)
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return future_price_pred[0]
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except Exception as e:
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@ -95,4 +95,4 @@ def update():
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if __name__ == "__main__":
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update_data()
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app.run(host="0.0.0.0", port=8127)
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app.run(host="0.0.0.0", port=8080)
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13
config.py
13
config.py
@ -2,4 +2,15 @@ import os
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app_base_path = os.getenv("APP_BASE_PATH", default=os.getcwd())
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data_base_path = os.path.join(app_base_path, "data")
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model_file_path = os.path.join(data_base_path, "model.pkl")
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model_file_path = {
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"ETH": os.path.join(data_base_path, "eth_model.pkl"),
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"BTC": os.path.join(data_base_path, "btc_model.pkl"),
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"SOL": os.path.join(data_base_path, "sol_model.pkl"),
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"BNB": os.path.join(data_base_path, "bnb_model.pkl"),
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"ARB": os.path.join(data_base_path, "arb_model.pkl"),
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}
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def get_training_data_path(token):
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return os.path.join(data_base_path, f"{token.lower()}_price_data.csv")
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70
model.py
70
model.py
@ -10,13 +10,19 @@ from updater import download_binance_monthly_data, download_binance_daily_data
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from config import data_base_path, model_file_path
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binance_data_path = os.path.join(data_base_path, "binance/futures-klines")
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training_price_data_path = os.path.join(data_base_path, "eth_price_data.csv")
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def get_training_data_path(token):
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"""
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Возвращает путь к файлу данных для указанного токена.
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"""
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return os.path.join(data_base_path, f"{token}_price_data.csv")
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def download_data():
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cm_or_um = "um"
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symbols = ["ETHUSDT"]
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intervals = ["10min"]
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symbols = ["ETHUSDT", "BTCUSDT", "SOLUSDT", "BNBUSDT", "ARBUSDT"]
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intervals = ["10min", "1d"]
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years = ["2020", "2021", "2022", "2023", "2024"]
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months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
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download_path = binance_data_path
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@ -33,8 +39,10 @@ def download_data():
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print(f"Downloaded daily data to {download_path}.")
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def format_data():
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files = sorted([x for x in os.listdir(binance_data_path) if x.endswith(".zip")])
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def format_data(token):
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files = sorted(
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[x for x in os.listdir(binance_data_path) if x.endswith(".zip") and token in x]
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)
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if len(files) == 0:
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return
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@ -75,10 +83,12 @@ def format_data():
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price_df.dropna(inplace=True)
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# Сохраняем данные
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training_price_data_path = get_training_data_path(token)
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price_df.sort_index().to_csv(training_price_data_path)
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def train_model():
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def train_model(token):
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training_price_data_path = get_training_data_path(token)
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price_data = pd.read_csv(training_price_data_path)
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# Используем дополнительные признаки
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@ -98,57 +108,17 @@ def train_model():
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x, y, test_size=0.2, random_state=0
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)
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# Train the model
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print("Training model...")
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model = XGBRegressor()
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model.fit(x_train, y_train)
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print("Model trained.")
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os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
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token_model_path = model_file_path[token]
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os.makedirs(os.path.dirname(token_model_path), exist_ok=True)
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with open(model_file_path, "wb") as f:
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with open(token_model_path, "wb") as f:
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pickle.dump(model, f)
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print(f"Trained model saved to {model_file_path}")
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print(f"Trained model saved to {token_model_path}")
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# Optional: Оценка модели
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y_pred = model.predict(x_test)
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print(f"Mean Absolute Error: {np.mean(np.abs(y_test - y_pred))}")
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def get_inference_data(token, period):
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"""
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Генерирует данные для инференса на основе переданного токена и периода.
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"""
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price_data = pd.read_csv(training_price_data_path)
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# Настроить разницу времени в зависимости от периода
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time_delta_map = {
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"1min": timedelta(minutes=1),
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"5min": timedelta(minutes(5)),
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"10min": timedelta(minutes(10)),
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"30min": timedelta(minutes(30)),
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"1h": timedelta(hours=1),
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"1d": timedelta(days=1),
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}
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# Получаем последний таймстамп и вычисляем следующий
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last_timestamp = pd.to_datetime(price_data["timestamp"].iloc[-1], unit="s")
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next_timestamp = last_timestamp + time_delta_map.get(period, timedelta(minutes=10))
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# Используем последние значения признаков
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last_data = price_data.iloc[-1]
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X_new = np.array(
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[
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[
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next_timestamp.timestamp(),
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last_data["price_diff"],
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last_data["volatility"],
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last_data["volume"],
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last_data["moving_avg_7"],
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last_data["moving_avg_30"],
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]
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]
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)
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return X_new
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