| Note : Before run the code make sure u are run in virtual env

• how to create goto the bottom of this readme

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| New Model are aviable in the test1 | With pre trained model for it , check that out

to run the code change dir

cd Stock-Ai/test1
python app.py

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In stock price prediction, short-term, mid-term, and long-term refer to different time horizons over which predictions are made, and each has a specific range based on typical trading and investing strategies:

1. Short-Term (Days to 1 Week)

• Range: 1 to 7 days ahead.
• Typical Use: Used by day traders or swing traders who aim to profit from small, quick price changes within a few days.
• Prediction Frequency: Daily predictions, often requiring more sensitive models like LSTMs that can capture daily price fluctuations and short-term market momentum.
• Indicators: Momentum indicators (e.g., RSI, MACD) are usually more relevant for short-term predictions.

2. Mid-Term (1 Week to 1 Month)

• Range: 1 to 4 weeks ahead.
• Typical Use: Used by swing traders and investors who aim to capture broader trends over several weeks, balancing short-term fluctuations with more stable price movement.
• Prediction Frequency: Weekly predictions; may use models that focus on capturing trends while smoothing out daily volatility.
• Indicators: Trend-following indicators (e.g., Moving Averages) are generally suitable for mid-term analysis, helping predict continuation or reversal of trends.

3. Long-Term (1 Month to 1 Year or More)

• Range: 1 month to 12 months (or even longer).
• Typical Use: Used by long-term investors or position traders who focus on the fundamental value of the stock and expect larger trends to play out over months or years.
• Prediction Frequency: Monthly predictions; long-term models often incorporate broader economic indicators and ignore minor fluctuations.
• Indicators: Longer-term moving averages (e.g., 50-day, 200-day), sentiment indicators, and fundamental data (earnings, P/E ratio) can be useful.

Summary Table

Term	Range	Prediction Interval	Use Case	Recommended Indicators
Short-Term	1 to 7 days	Daily	Day trading, short-term trades	RSI, MACD, short MAs
Mid-Term	1 week to 1 month	Weekly	Swing trading	Moving Averages, trend lines
Long-Term	1 month to 1 year+	Monthly	Position trading, long-term hold	50-day/200-day MAs, fundamentals

Each timeframe captures different price movements and may benefit from different models and indicators. In your project, training specific models for each timeframe and customizing features for each range can improve accuracy and relevance. Let me know if you'd like further details on any specific timeframe!

Test area

curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d '{
        "ticker": "ADSL.NS",
        "start": "2024-07-01",
        "end": "2024-11-13",
        "timeframe": "short-term"
    }'

1. Dynamic Risk Percentage Instead of using a fixed risk percentage, dynamically calculate it based on market conditions, stock volatility, or user preferences.

Implementation: Volatility-Based Risk: Use indicators like Average True Range (ATR) or standard deviation of recent prices to determine the risk percentage. Risk Percentage

ATR Current Price Risk Percentage= Current Price ATR ​

User-Defined Levels: Allow users to choose between low, medium, or high risk, and map these to predefined risk percentages (e.g., 2%, 5%, 10%).

1. Using venv (Python 3 built-in module):

Step-by-Step:

1. Navigate to your project folder:

   cd /path/to/your/project

2. Create a virtual environment:

   python3 -m venv env_name

   Replace env_name with the desired name for your virtual environment (e.g., venv).

3. Activate the virtual environment:

   • On macOS/Linux:

     source env_name/bin/activate

   • On Windows (Command Prompt):

     env_name\Scripts\activate

   • On Windows (PowerShell):

     .\env_name\Scripts\Activate.ps1

4. Install dependencies within the environment:

   pip install <package_name>

5. Deactivate the virtual environment (when done):

   deactivate