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import gradio as gr |
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import yfinance as yf |
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import pandas as pd |
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from sklearn.linear_model import LinearRegression |
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import plotly.graph_objects as go |
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markdown_content = """ |
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# Asset Price Prediction Tool |
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## Introduction |
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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. |
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## How to Use |
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1. **Enter the Ticker Symbol:** Input the stock ticker (e.g., 'AAPL' for Apple Inc.). |
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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). |
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3. **Set Prediction Days:** Decide how many days into the future you want to predict. |
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4. **Submit:** Click 'Run' to view the predictions. |
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## How It Works |
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- **Data Fetching:** The tool fetches historical closing prices of the specified asset using `yfinance` for the date range you provide. |
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- **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. |
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- **Making Predictions:** Based on the learned relationship, the model attempts to predict future prices for the number of days you specified. |
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## Understanding Linear Regression |
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- Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables. |
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- In this tool, the dependent variable is the asset's price, and the independent variable is time (dates). |
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- The model assumes a linear relationship (a straight line trend) between dates and prices. |
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- 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. |
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## Interpreting Data |
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- **Historical Prices:** Displayed as a solid blue line, representing actual past closing prices. |
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- **Predicted Prices:** Shown as a solid red line, indicating the model's predictions. |
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- **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. |
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Remember, investing in the stock market involves risks, and past performance is not indicative of future results. |
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""" |
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def train_predict_wrapper(ticker, start_date, end_date, prediction_days): |
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data = yf.download(ticker, start=start_date, end=end_date) |
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data = data["Close"] |
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data.index = (data.index - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') |
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X = data.index.values[:-prediction_days].reshape(-1, 1) |
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y = data.values[:-prediction_days] |
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model = LinearRegression() |
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model.fit(X, y) |
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last_timestamp = data.index[-1] |
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future_timestamps = pd.date_range(start=pd.to_datetime(last_timestamp, unit='s'), |
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periods=prediction_days, freq='D') |
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future_timestamps = (future_timestamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') |
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X_future = future_timestamps.values.reshape(-1, 1) |
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predicted_prices = model.predict(X_future) |
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historical_prices = go.Scatter( |
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x=pd.to_datetime(data.index, unit='s'), |
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y=data.values, |
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mode="lines", |
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name="Historical Prices" |
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) |
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predicted_prices_trace = go.Scatter( |
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x=pd.to_datetime(future_timestamps, unit='s'), |
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y=predicted_prices, |
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mode="lines", |
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name="Predicted Prices" |
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) |
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fig = go.Figure() |
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fig.add_trace(historical_prices) |
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fig.add_trace(predicted_prices_trace) |
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fig.update_layout( |
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title="Asset Price Prediction", |
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xaxis_title="Date", |
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yaxis_title="Price", |
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legend_title_text="Data" |
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) |
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return fig |
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interface = gr.Interface( |
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fn=train_predict_wrapper, |
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inputs=[ |
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gr.Textbox(label="Ticker Symbol"), |
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gr.Textbox(label="Start Date (YYYY-MM-DD)"), |
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gr.Textbox(label="End Date (YYYY-MM-DD)"), |
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gr.Slider(minimum=1, maximum=365, step=1, label="Prediction Days"), |
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], |
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outputs="plot", |
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description=markdown_content |
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) |
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interface.launch() |
