105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
import os
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import pickle
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from zipfile import ZipFile
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from datetime import datetime
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from updater import download_binance_monthly_data, download_binance_daily_data
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from config import app_base_path, model_file_path
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binance_data_path = os.path.join(app_base_path, "binance/futures-klines")
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training_price_data_path = os.path.join(app_base_path, "eth_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 = ["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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download_binance_monthly_data(
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cm_or_um, symbols, intervals, years, months, download_path
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)
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print(f"Downloaded monthly data to {download_path}.")
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current_datetime = datetime.now()
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current_year = current_datetime.year
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current_month = current_datetime.month
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download_binance_daily_data(
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cm_or_um, symbols, intervals, current_year, current_month, download_path
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)
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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)])
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# No files to process
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if len(files) == 0:
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return
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price_df = pd.DataFrame()
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for file in files:
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zip_file_path = os.path.join(binance_data_path, file)
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if not zip_file_path.endswith(".zip"):
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continue
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myzip = ZipFile(zip_file_path)
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with myzip.open(myzip.filelist[0]) as f:
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line = f.readline()
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header = 0 if line.decode("utf-8").startswith("open_time") else None
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df = pd.read_csv(myzip.open(myzip.filelist[0]), header=header).iloc[:, :11]
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df.columns = [
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"start_time",
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"open",
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"high",
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"low",
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"close",
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"volume",
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"end_time",
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"volume_usd",
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"n_trades",
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"taker_volume",
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"taker_volume_usd",
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]
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df.index = [pd.Timestamp(x + 1, unit="ms") for x in df["end_time"]]
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df.index.name = "date"
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price_df = pd.concat([price_df, df])
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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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# Load the eth price data
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price_data = pd.read_csv(training_price_data_path)
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df = pd.DataFrame()
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# Convert 'date' to a numerical value (timestamp) we can use for regression
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df["date"] = pd.to_datetime(price_data["date"])
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df["date"] = df["date"].map(pd.Timestamp.timestamp)
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df["price"] = price_data[["open", "close", "high", "low"]].mean(axis=1)
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# Reshape the data to the shape expected by sklearn
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x = df["date"].values.reshape(-1, 1)
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y = df["price"].values.reshape(-1, 1)
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# Split the data into training set and test set
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x_train, _, y_train, _ = train_test_split(x, y, test_size=0.2, random_state=0)
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# Train the model
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model = LinearRegression()
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model.fit(x_train, y_train)
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# create the model's parent directory if it doesn't exist
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os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
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# Save the trained model to a file
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with open(model_file_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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