allora/model.py

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Python
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2024-08-24 02:33:19 +03:00
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}")