add many tokens

app.py

@@ -4,64 +4,78 @@ 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()
-    train_model()
+    for token in tokens:
+        format_data(token)
+        train_model(token)
 
 
-def get_eth_inference():
-    """Load model and predict current price."""
+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
-        X_new = (
-            price_data[
-                [
-                    "timestamp",
-                    "price_diff",
-                    "volatility",
-                    "volume",
-                    "moving_avg_7",
-                    "moving_avg_30",
-                ]
+        last_row = price_data.iloc[-1]
+        last_timestamp = last_row["timestamp"]
+
+        # Преобразуем период в секунды
+        period_seconds = convert_period_to_seconds(period)
+        new_timestamp = last_timestamp + period_seconds
+
+        # Формируем данные для предсказания с новым timestamp
+        X_new = np.array(
+            [
+                new_timestamp,
+                last_row["price_diff"],
+                last_row["volatility"],
+                last_row["volume"],
+                last_row["moving_avg_7"],
+                last_row["moving_avg_30"],
             ]
-            .iloc[-1]
-            .values.reshape(1, -1)
-        )
+        ).reshape(1, -1)
 
         # Делаем предсказание
-        current_price_pred = loaded_model.predict(X_new)
+        future_price_pred = loaded_model.predict(X_new)
+        return future_price_pred[0]
 
-        return current_price_pred[0]
     except Exception as e:
         print(f"Error during inference: {str(e)}")
         raise
 
 
-@app.route("/inference/<string:token>")
-def generate_inference(token):
-    """Generate inference for given token."""
-    if not token or token != "ETH":
-        error_msg = "Token is required" if not token else "Token not supported"
-        return Response(
-            json.dumps({"error": error_msg}), status=400, mimetype="application/json"
-        )
+def convert_period_to_seconds(period):
+    """Конвертируем период в секунды."""
+    if period.endswith("min"):
+        return int(period[:-3]) * 60
+    elif period.endswith("h"):
+        return int(period[:-1]) * 3600
+    elif period.endswith("d"):
+        return int(period[:-1]) * 86400
+    else:
+        raise ValueError(f"Unknown period format: {period}")
 
+
+@app.route("/inference/<string:token>/<string:period>")
+def generate_inference(token, period):
+    """Generate inference for given token and period."""
     try:
-        inference = get_eth_inference()
+        inference = get_inference(token, period)
         return Response(str(inference), status=200)
     except Exception as e:
         return Response(
@@ -81,4 +95,4 @@ def update():
 
 if __name__ == "__main__":
     update_data()
-    app.run(host="0.0.0.0", port=8000)
+    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

@@ -3,20 +3,26 @@ import pickle
 import numpy as np
 from xgboost import XGBRegressor
 from zipfile import ZipFile
-from datetime import datetime
+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 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"]
-    intervals = ["1d"]
+    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
@@ -33,8 +39,10 @@ def download_data():
     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")])
+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
@@ -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
@@ -98,20 +108,16 @@ 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.")
 
-    # create the model's parent directory if it doesn't exist
-    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)
 
-    # Save the trained model to a file
-    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)