add many tokens

app.py

@@ -4,40 +4,41 @@ import pandas as pd
 import numpy as np
 from datetime import datetime
 from flask import Flask, jsonify, Response
-from model import download_data, format_data, train_model, training_price_data_path
+from model import download_data, format_data, train_model, get_training_data_path
 from config import model_file_path
 
 app = Flask(__name__)
 
 
 def update_data():
-    """Download price data, format data and train model."""
+    """Download price data, format data and train model for each token."""
+    tokens = ["ETH", "BTC", "SOL", "BNB", "ARB"]
     download_data()
-    format_data()
+    for token in tokens:
-    train_model()
+        format_data(token)
+        train_model(token)
 
 
 def get_inference(token, period):
     try:
-        with open(model_file_path, "rb") as f:
+        model_path = model_file_path[token]
+
+        with open(model_path, "rb") as f:
             loaded_model = pickle.load(f)
 
-        # Загружаем последние данные из файла
+        # Загружаем последние данные для данного токена
+        training_price_data_path = get_training_data_path(token)
         price_data = pd.read_csv(training_price_data_path)
 
         # Используем последние значения признаков для предсказания
         last_row = price_data.iloc[-1]
-
-        # Получаем последний timestamp
         last_timestamp = last_row["timestamp"]
 
-        # Преобразуем период в секунды (пример)
+        # Преобразуем период в секунды
         period_seconds = convert_period_to_seconds(period)
-
-        # Рассчитываем новый временной штамп на основе периода
         new_timestamp = last_timestamp + period_seconds
 
-        # timestamp
+        # Формируем данные для предсказания с новым timestamp
         X_new = np.array(
             [
                 new_timestamp,
@@ -49,9 +50,8 @@ def get_inference(token, period):
             ]
         ).reshape(1, -1)
 
-        # Предсказание
+        # Делаем предсказание
         future_price_pred = loaded_model.predict(X_new)
-
         return future_price_pred[0]
 
     except Exception as e:
@@ -95,4 +95,4 @@ def update():
 
 if __name__ == "__main__":
     update_data()
-    app.run(host="0.0.0.0", port=8127)
+    app.run(host="0.0.0.0", port=8080)

config.py

@@ -2,4 +2,15 @@ import os
 
 app_base_path = os.getenv("APP_BASE_PATH", default=os.getcwd())
 data_base_path = os.path.join(app_base_path, "data")
-model_file_path = os.path.join(data_base_path, "model.pkl")
+
+model_file_path = {
+    "ETH": os.path.join(data_base_path, "eth_model.pkl"),
+    "BTC": os.path.join(data_base_path, "btc_model.pkl"),
+    "SOL": os.path.join(data_base_path, "sol_model.pkl"),
+    "BNB": os.path.join(data_base_path, "bnb_model.pkl"),
+    "ARB": os.path.join(data_base_path, "arb_model.pkl"),
+}
+
+
+def get_training_data_path(token):
+    return os.path.join(data_base_path, f"{token.lower()}_price_data.csv")

model.py

@@ -10,13 +10,19 @@ 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 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"]
+    symbols = ["ETHUSDT", "BTCUSDT", "SOLUSDT", "BNBUSDT", "ARBUSDT"]
-    intervals = ["10min"]
+    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
@@ -33,8 +39,10 @@ def download_data():
     print(f"Downloaded daily data to {download_path}.")
 
 
-def format_data():
+def format_data(token):
-    files = sorted([x for x in os.listdir(binance_data_path) if x.endswith(".zip")])
+    files = sorted(
+        [x for x in os.listdir(binance_data_path) if x.endswith(".zip") and token in x]
+    )
 
     if len(files) == 0:
         return
@@ -75,10 +83,12 @@ def format_data():
     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():
+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
@@ -98,57 +108,17 @@ def train_model():
         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)
+    token_model_path = model_file_path[token]
+    os.makedirs(os.path.dirname(token_model_path), exist_ok=True)
 
-    with open(model_file_path, "wb") as f:
+    with open(token_model_path, "wb") as f:
         pickle.dump(model, f)
 
-    print(f"Trained model saved to {model_file_path}")
+    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))}")
-
-
-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