new model

This commit is contained in:
vvzvlad 2024-09-03 04:24:43 +03:00
parent c7cc0079a8
commit 14e8c74962
3 changed files with 80 additions and 34 deletions

44
app.py
View File

@ -4,7 +4,7 @@ import pandas as pd
import numpy as np import numpy as np
from datetime import datetime from datetime import datetime
from flask import Flask, jsonify, Response from flask import Flask, jsonify, Response
from model import download_data, format_data, train_model from model import download_data, format_data, train_model, training_price_data_path
from config import model_file_path from config import model_file_path
app = Flask(__name__) app = Flask(__name__)
@ -19,14 +19,36 @@ def update_data():
def get_eth_inference(): def get_eth_inference():
"""Load model and predict current price.""" """Load model and predict current price."""
with open(model_file_path, "rb") as f: try:
loaded_model = pickle.load(f) with open(model_file_path, "rb") as f:
loaded_model = pickle.load(f)
now_timestamp = pd.Timestamp(datetime.now()).timestamp() # Загружаем последние данные из файла
X_new = np.array([now_timestamp]).reshape(-1, 1) price_data = pd.read_csv(training_price_data_path)
current_price_pred = loaded_model.predict(X_new)
return current_price_pred[0] # Используем последние значения признаков для предсказания
X_new = (
price_data[
[
"timestamp",
"price_diff",
"volatility",
"volume",
"moving_avg_7",
"moving_avg_30",
]
]
.iloc[-1]
.values.reshape(1, -1)
)
# Делаем предсказание
current_price_pred = loaded_model.predict(X_new)
return current_price_pred[0]
except Exception as e:
print(f"Error during inference: {str(e)}")
raise
@app.route("/inference/<string:token>") @app.route("/inference/<string:token>")
@ -34,13 +56,17 @@ def generate_inference(token):
"""Generate inference for given token.""" """Generate inference for given token."""
if not token or token != "ETH": if not token or token != "ETH":
error_msg = "Token is required" if not token else "Token not supported" error_msg = "Token is required" if not token else "Token not supported"
return Response(json.dumps({"error": error_msg}), status=400, mimetype='application/json') return Response(
json.dumps({"error": error_msg}), status=400, mimetype="application/json"
)
try: try:
inference = get_eth_inference() inference = get_eth_inference()
return Response(str(inference), status=200) return Response(str(inference), status=200)
except Exception as e: except Exception as e:
return Response(json.dumps({"error": str(e)}), status=500, mimetype='application/json') return Response(
json.dumps({"error": str(e)}), status=500, mimetype="application/json"
)
@app.route("/update") @app.route("/update")

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@ -1,15 +1,14 @@
import os import os
import pickle import pickle
import numpy as np
from xgboost import XGBRegressor
from zipfile import ZipFile from zipfile import ZipFile
from datetime import datetime from datetime import datetime
import pandas as pd import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split 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 updater import download_binance_monthly_data, download_binance_daily_data
from config import data_base_path, model_file_path from config import data_base_path, model_file_path
binance_data_path = os.path.join(data_base_path, "binance/futures-klines") 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") training_price_data_path = os.path.join(data_base_path, "eth_price_data.csv")
@ -35,19 +34,14 @@ def download_data():
def format_data(): def format_data():
files = sorted([x for x in os.listdir(binance_data_path)]) files = sorted([x for x in os.listdir(binance_data_path) if x.endswith(".zip")])
# No files to process
if len(files) == 0: if len(files) == 0:
return return
price_df = pd.DataFrame() price_df = pd.DataFrame()
for file in files: for file in files:
zip_file_path = os.path.join(binance_data_path, file) zip_file_path = os.path.join(binance_data_path, file)
if not zip_file_path.endswith(".zip"):
continue
myzip = ZipFile(zip_file_path) myzip = ZipFile(zip_file_path)
with myzip.open(myzip.filelist[0]) as f: with myzip.open(myzip.filelist[0]) as f:
line = f.readline() line = f.readline()
@ -70,30 +64,43 @@ def format_data():
df.index.name = "date" df.index.name = "date"
price_df = pd.concat([price_df, df]) price_df = pd.concat([price_df, df])
price_df["timestamp"] = price_df.index.map(pd.Timestamp.timestamp)
price_df["price_diff"] = price_df["close"].diff()
price_df["volatility"] = (price_df["high"] - price_df["low"]) / price_df["open"]
price_df["volume"] = price_df["volume"]
price_df["moving_avg_7"] = price_df["close"].rolling(window=7).mean()
price_df["moving_avg_30"] = price_df["close"].rolling(window=30).mean()
# Удаляем строки с NaN значениями
price_df.dropna(inplace=True)
# Сохраняем данные
price_df.sort_index().to_csv(training_price_data_path) price_df.sort_index().to_csv(training_price_data_path)
def train_model(): def train_model():
# Load the eth price data
price_data = pd.read_csv(training_price_data_path) price_data = pd.read_csv(training_price_data_path)
df = pd.DataFrame()
# Convert 'date' to a numerical value (timestamp) we can use for regression # Используем дополнительные признаки
df["date"] = pd.to_datetime(price_data["date"]) x = price_data[
df["date"] = df["date"].map(pd.Timestamp.timestamp) [
"timestamp",
"price_diff",
"volatility",
"volume",
"moving_avg_7",
"moving_avg_30",
]
]
y = price_data["close"]
df["price"] = price_data[["open", "close", "high", "low"]].mean(axis=1) x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, random_state=0
# 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 # Train the model
print("Training model...") print("Training model...")
model = linear_model.Lasso(alpha=0.1) model = XGBRegressor()
model.fit(x_train, y_train) model.fit(x_train, y_train)
print("Model trained.") print("Model trained.")
@ -105,3 +112,7 @@ def train_model():
pickle.dump(model, f) pickle.dump(model, f)
print(f"Trained model saved to {model_file_path}") print(f"Trained model saved to {model_file_path}")
# Optional: Оценка модели
y_pred = model.predict(x_test)
print(f"Mean Absolute Error: {np.mean(np.abs(y_test - y_pred))}")

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@ -5,3 +5,12 @@ pandas==2.1.3
Requests==2.32.0 Requests==2.32.0
scikit_learn==1.3.2 scikit_learn==1.3.2
werkzeug>=3.0.3 # not directly required, pinned by Snyk to avoid a vulnerability werkzeug>=3.0.3 # not directly required, pinned by Snyk to avoid a vulnerability
itsdangerous
Jinja2
MarkupSafe
python-dateutil
pytz
scipy
six
sklearn
xgboost