File size: 2,285 Bytes
a2d3623 1e716d6 ed74d2e 1e716d6 a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e a2d3623 ddd2d15 ed74d2e a2d3623 ed74d2e a2d3623 ed74d2e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import gradio as gr
import yfinance as yf
from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
def train_predict_wrapper(ticker, start_date, end_date, prediction_days):
"""
Downloads stock data, trains a linear regression model, and predicts future prices.
Args:
ticker: The ticker symbol of the stock.
start_date: The start date for the data (YYYY-MM-DD format).
end_date: The end date for the data (YYYY-MM-DD format).
prediction_days: The number of days to predict.
Returns:
A plotly figure with the historical and predicted prices.
"""
# Download stock data
data = yf.download(ticker, start=start_date, end=end_date)
data = data["Close"]
# Train linear regression model
X = data.index.values[:-prediction_days].reshape(-1, 1)
y = data.values[:-prediction_days]
model = LinearRegression()
model.fit(X, y)
# Predict future prices
future_dates = data.index.values[-prediction_days:]
X_future = future_dates.reshape(-1, 1)
predicted_prices = model.predict(X_future)
# Prepare data for plotting
historical_prices = go.Scatter(
x=data.index,
y=data.values,
mode="lines",
line_color=lambda p: "green" if p > 0 else "red",
name="Historical Prices",
)
predicted_prices_trace = go.Scatter(
x=future_dates,
y=predicted_prices,
mode="lines",
line_color="gold",
line_width=3,
marker_line_width=3,
marker_color="black",
name="Predicted Prices",
)
# Plot data
fig = go.Figure()
fig.add_trace(historical_prices)
fig.add_trace(predicted_prices_trace)
fig.update_layout(
title="Stock Price Prediction",
xaxis_title="Date",
yaxis_title="Price",
legend_title_text="Data",
)
return fig
# Define Gradio interface
interface = gr.Interface(
fn=train_predict_wrapper,
inputs=[
gr.Textbox(label="Ticker Symbol"),
gr.Textbox(label="Start Date (YYYY-MM-DD)"),
gr.Textbox(label="End Date (YYYY-MM-DD)"),
gr.Slider(minimum=1, maximum=30, step=1, label="Prediction Days"),
],
outputs="plot",
)
# Launch the app
interface.launch() |