Financial AI Bot Development
Budget: ₹12,500 – ₹37,500 INR
**AI Bot: Strategy and Automation Framework**
**Objectives**
- AI communication
- Strategies automation for trading financial assets
**AI Model Blueprint**
- Use historical price action data: Focus on major price moves above 2%.
- Identify why price surges or drops occur (when price shifts more than 2% in up to 4 hours).
- Select appropriate assets (pairs, coins, stocks, indices) with high volume and good leverage opportunities.
- Apply the best AI training models.
- Integrate reliable news APIs (e.g., Twitter, Finance platforms) for news input and sentiment.
**Training Approach**
- Train the AI model with historical data by mapping news events from APIs to the respective historical moments.
- Match news content with price changes in the historical data and analyze the correspondence.
- If the price impact is more than 2%, let the AI analyze whether the connected news was positive or negative.
**Signal and Strategy Logic**
- AI bot gives a signal to the trading strategy. If a signal is already triggered, check percentage change before signaling again.
- Assess how much the asset has spiked (between 2% and 17%) and trigger trade with a stop loss if conditions are met.
**Strategy Categorization**
- Stocks: With options: Buy/Sell; Normal: Buy
- Indices: Approach similar to stocks.
- Crypto: Use leverage.
- Forex: Use leverage.
**System Implementation**
- Build the AI bot in Python with a laddering system.
- Develop and maintain five codes compatible with various countries and brokers.
**Additional Best Practices**
- Filter for only high-volume, liquid assets.
- Use diverse AI models, including advanced natural language processing for sentiment analysis.
- Regularly backtest the system with new data.
- Implement adaptive risk management, including customized stop-loss and take-profit per asset and market condition.
- Ensure scalability and regulatory compliance across markets.
**Objectives**
- AI communication
- Strategies automation for trading financial assets
**AI Model Blueprint**
- Use historical price action data: Focus on major price moves above 2%.
- Identify why price surges or drops occur (when price shifts more than 2% in up to 4 hours).
- Select appropriate assets (pairs, coins, stocks, indices) with high volume and good leverage opportunities.
- Apply the best AI training models.
- Integrate reliable news APIs (e.g., Twitter, Finance platforms) for news input and sentiment.
**Training Approach**
- Train the AI model with historical data by mapping news events from APIs to the respective historical moments.
- Match news content with price changes in the historical data and analyze the correspondence.
- If the price impact is more than 2%, let the AI analyze whether the connected news was positive or negative.
**Signal and Strategy Logic**
- AI bot gives a signal to the trading strategy. If a signal is already triggered, check percentage change before signaling again.
- Assess how much the asset has spiked (between 2% and 17%) and trigger trade with a stop loss if conditions are met.
**Strategy Categorization**
- Stocks: With options: Buy/Sell; Normal: Buy
- Indices: Approach similar to stocks.
- Crypto: Use leverage.
- Forex: Use leverage.
**System Implementation**
- Build the AI bot in Python with a laddering system.
- Develop and maintain five codes compatible with various countries and brokers.
**Additional Best Practices**
- Filter for only high-volume, liquid assets.
- Use diverse AI models, including advanced natural language processing for sentiment analysis.
- Regularly backtest the system with new data.
- Implement adaptive risk management, including customized stop-loss and take-profit per asset and market condition.
- Ensure scalability and regulatory compliance across markets.