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import gradio as gr
import yfinance as yf
from prophet import Prophet
from sklearn.linear_model import LinearRegression
import pandas as pd
from datetime import datetime
import plotly.graph_objects as go
def download_data(ticker, start_date='2010-01-01'):
"""
์ฃผ์ ๋ฐ์ดํฐ๋ฅผ ๋ค์ด๋ก๋ํ๊ณ ํฌ๋งท์ ์กฐ์ ํ๋ ํจ์
"""
data = yf.download(ticker, start=start_date)
if data.empty:
raise ValueError(f"No data returned for {ticker}")
data.reset_index(inplace=True)
if 'Adj Close' in data.columns:
data = data[['Date', 'Adj Close']]
data.rename(columns={'Date': 'ds', 'Adj Close': 'y'}, inplace=True)
else:
raise ValueError("Expected 'Adj Close' in columns")
return data
def predict_future_prices(ticker, periods=1825):
data = download_data(ticker)
# Prophet ๋ชจ๋ธ ์์ฑ ๋ฐ ํ์ต
model_prophet = Prophet(daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=True)
model_prophet.fit(data)
# ๋ฏธ๋ ๋ฐ์ดํฐ ํ๋ ์ ์์ฑ ๋ฐ ์์ธก
future = model_prophet.make_future_dataframe(periods=periods, freq='D')
forecast_prophet = model_prophet.predict(future)
# Linear Regression ๋ชจ๋ธ ์์ฑ ๋ฐ ํ์ต
model_lr = LinearRegression()
X = pd.to_numeric(pd.Series(range(len(data))))
y = data['y'].values
model_lr.fit(X.values.reshape(-1, 1), y)
# ๋ฏธ๋ ๋ฐ์ดํฐ ํ๋ ์ ์์ฑ ๋ฐ ์์ธก
future_dates = pd.date_range(start=data['ds'].iloc[-1], periods=periods+1, freq='D')[1:]
future_lr = pd.DataFrame({'ds': future_dates})
future_lr['ds'] = future_lr['ds'].dt.strftime('%Y-%m-%d')
X_future = pd.to_numeric(pd.Series(range(len(data), len(data) + len(future_lr))))
future_lr['yhat'] = model_lr.predict(X_future.values.reshape(-1, 1))
# ์์ธก ๊ฒฐ๊ณผ ๊ทธ๋ํ ์์ฑ
forecast_prophet['ds'] = forecast_prophet['ds'].dt.strftime('%Y-%m-%d')
fig = go.Figure()
fig.add_trace(go.Scatter(x=forecast_prophet['ds'], y=forecast_prophet['yhat'], mode='lines', name='Prophet Forecast (Blue)'))
fig.add_trace(go.Scatter(x=future_lr['ds'], y=future_lr['yhat'], mode='lines', name='Linear Regression Forecast (Red)', line=dict(color='red')))
fig.add_trace(go.Scatter(x=data['ds'], y=data['y'], mode='lines', name='Actual (Black)', line=dict(color='black')))
return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']]
css = """footer { visibility: hidden; }"""
with gr.Blocks(css=css) as app:
gr.Markdown("""
<style>
.markdown-text h2 {
font-size: 12px; # ํฐํธ ํฌ๊ธฐ๋ฅผ 12px๋ก ์ค์
}
</style>
<h2>AIQ StockAI: ๊ธ๋ก๋ฒ ์์ฐ(์ฃผ์, ์ง์, BTC, ์ํ ๋ฑ) ๋ฏธ๋ ์ฃผ๊ฐ ์์ธก AI ์๋น์ค</h2>
<h2>์ ์ธ๊ณ ๋ชจ๋ ํฐ์ปค ๋ณด๊ธฐ(์ผํ ํ์ด๋ธ์ค): <a href="https://finance.yahoo.com/most-active" target="_blank">์ฌ๊ธฐ๋ฅผ ํด๋ฆญ</a></h2>
""")
with gr.Row():
ticker_input = gr.Textbox(value="NVDA", label="Enter Stock Ticker for Forecast")
periods_input = gr.Number(value=1825, label="Forecast Period (days)")
forecast_button = gr.Button("Generate Forecast")
forecast_chart = gr.Plot(label="Forecast Chart")
forecast_data_prophet = gr.Dataframe(label="Prophet Forecast Data")
forecast_data_lr = gr.Dataframe(label="Linear Regression Forecast Data")
forecast_button.click(
fn=predict_future_prices,
inputs=[ticker_input, periods_input],
outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr]
)
app.launch() |