Add model-lasso.py

model-lasso.py

@@ -0,0 +1,107 @@
+import os
+import pickle
+from zipfile import ZipFile
+from datetime import datetime
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn import linear_model
+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 = ["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():
+    files = sorted([x for x in os.listdir(binance_data_path)])
+
+    # No files to process
+    if len(files) == 0:
+        return
+
+    price_df = pd.DataFrame()
+    for file in files:
+        zip_file_path = os.path.join(binance_data_path, file)
+
+        if not zip_file_path.endswith(".zip"):
+            continue
+
+        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.sort_index().to_csv(training_price_data_path)
+
+
+def train_model():
+    # Load the eth price data
+    price_data = pd.read_csv(training_price_data_path)
+    df = pd.DataFrame()
+
+    # Convert 'date' to a numerical value (timestamp) we can use for regression
+    df["date"] = pd.to_datetime(price_data["date"])
+    df["date"] = df["date"].map(pd.Timestamp.timestamp)
+
+    df["price"] = price_data[["open", "close", "high", "low"]].mean(axis=1)
+
+    # Reshape the data to the shape expected by sklearn
+    x = df["date"].values.reshape(-1, 1)
+    y = df["price"].values.reshape(-1, 1)
+
+    # Split the data into training set and test set
+    x_train, _, y_train, _ = train_test_split(x, y, test_size=0.2, random_state=0)
+
+    # Train the model
+    print("Training model...")
+    model = linear_model.Lasso(alpha=0.1)
+    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)
+
+    # Save the trained model to a file
+    with open(model_file_path, "wb") as f:
+        pickle.dump(model, f)
+
+    print(f"Trained model saved to {model_file_path}")