app.py

import gradio as gr
import yfinance as yf
import pandas as pd
from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go

markdown_content = """
# Asset Price Prediction Tool

## Introduction
This tool uses historical stock price data to predict future prices. It's designed to provide insights into potential price trends based on past performance.

## How to Use
1. **Enter the Ticker Symbol:** Input the stock ticker (e.g., 'AAPL' for Apple Inc.).
2. **Select Start and End Dates:** Choose the historical data range for analysis. Dates must be entered in the format YYYY-MM-DD (e.g., 2023-01-01).
3. **Set Prediction Days:** Decide how many days into the future you want to predict.
4. **Submit:** Click 'Run' to view the predictions.

## How It Works
- **Data Fetching:** The tool fetches historical closing prices of the specified asset using `yfinance` for the date range you provide.
- **Model Training:** It then trains a linear regression model on this data. The model learns the relationship between dates and closing prices during this period.
- **Making Predictions:** Based on the learned relationship, the model attempts to predict future prices for the number of days you specified.

## Understanding Linear Regression
- Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables.
- In this tool, the dependent variable is the asset's price, and the independent variable is time (dates).
- The model assumes a linear relationship (a straight line trend) between dates and prices.
- It's important to note that this method works best when the relationship between the data points is linear and may not capture complex market dynamics.

## Interpreting Data
- **Historical Prices:** Displayed as a solid blue line, representing actual past closing prices.
- **Predicted Prices:** Shown as a solid red line, indicating the model's predictions.
- **Limitations:** The predictions are based on historical trends and do not account for unforeseen market events or changes in market conditions. They should be used as a guideline rather than definitive financial advice.

Remember, investing in the stock market involves risks, and past performance is not indicative of future results.
"""

def train_predict_wrapper(ticker, start_date, end_date, prediction_days):
    # Download asset data
    data = yf.download(ticker, start=start_date, end=end_date)
    data = data["Close"]

    # Convert index to Unix timestamp (seconds)
    data.index = (data.index - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')

    # Train linear regression model
    X = data.index.values[:-prediction_days].reshape(-1, 1)
    y = data.values[:-prediction_days]
    model = LinearRegression()
    model.fit(X, y)

    # Prepare data for prediction
    last_timestamp = data.index[-1]
    future_timestamps = pd.date_range(start=pd.to_datetime(last_timestamp, unit='s'), 
                                      periods=prediction_days, freq='D')
    future_timestamps = (future_timestamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
    X_future = future_timestamps.values.reshape(-1, 1)

    # Predict future prices
    predicted_prices = model.predict(X_future)

    # Prepare data for plotting
    historical_prices = go.Scatter(
        x=pd.to_datetime(data.index, unit='s'),
        y=data.values,
        mode="lines",
        name="Historical Prices"
    )
    predicted_prices_trace = go.Scatter(
        x=pd.to_datetime(future_timestamps, unit='s'),
        y=predicted_prices,
        mode="lines",
        name="Predicted Prices"
    )

    # Plot data
    fig = go.Figure()
    fig.add_trace(historical_prices)
    fig.add_trace(predicted_prices_trace)
    fig.update_layout(
        title="Asset 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=365, step=1, label="Prediction Days"),
    ],
    outputs="plot",
    description=markdown_content
)

# Launch the app
interface.launch()