Python AI Agent project - Built on Lightning.ai

I'm seeking a python developer to create versatile AI agents capable of performing assigned task such as data analysis, and others specified in the “tech description” The project will be built on [login to view URL] Key requirements: - Development of AI agents capable of performing data analysis, recommendation system tasks, and other functions. - Proficiency in Python, Java, and JavaScript is preferred, but other languages are acceptable if they are more suitable for the task. - Familiarity with TensorFlow, PyTorch, and Microsoft Cognitive, OpenAI I AM AN AMATEUR DAY TRADER (NON PROFITABLE) YET. ENSURE YOU GIVE YOUR VERY BEST OFFER, AS WE WON’T HAVE SPACE TO NEGOTIATE ANYTHING IF YOU TAKE A CHANCE. Much appreciated Please, see below a detailed breakdown of the project that I need on Lightning AI. It focuses on the integration of real-time data, AI agent building, market analysis, deployment, and reporting structure. The developer needs a deep knowledge/experience with Python, machine learning libraries, financial data APIs, and the Lightning AI platform. I will share The api’s key will be shared by myself, no need to create the api’s, just the integration and functionality will be done by the developer. I will also share the python code of the agent functions that I will create via chatGPT, that could need to be adapted by the developer. Environment Setup Install Lightning AI: Lightning AI can be installed using pip: bash Copy code pip install lightning, crewai, etc Set up Virtual Environment: Create and activate a virtual environment for managing dependencies: bash Copy code python3 -m venv trading_env source trading_env/bin/activate Install Dependencies: The following libraries should be installed: pandas, numpy for data manipulation requests or websockets for API calls (if using webhooks) scikit-learn, tensorflow, or pytorch-lightning for machine learning models matplotlib for visualization yfinance, alpaca_trade_api, or tdameritrade for market data APIs bash Copy code pip install pandas numpy requests pytorch-lightning matplotlib yfinance Market Data Integration Historical and Real-time Data Sources: Choose your market data providers, e.g., Yahoo Finance (yfinance), Alpaca, or TD Ameritrade APIs. For example, using Alpaca: python Copy code from [login to view URL] import REST, TimeFrame api = REST('API_KEY', 'API_SECRET', base_url='[login to view URL]') # Fetching historical data barset = api.get_barset('AAPL', [login to view URL], limit=100) aapl_bars = barset['AAPL'] Real-time Stock Data API: e.g., Alpaca, [login to view URL], IEX Cloud, Alpha Vantage, Finnhub, Historical Data API, yahoo finance, Alpha Vantage, Quandl, Tiingo, marketpsyp, Nasdaq APi among others. News & Sentiment Analysis API: e.g., NewsAPI, RavenPack, Google News API, X API, news api, stocksuit, Trading: TradingView, Bookmap, Interactive Brokers, robinhood, quantower, among others AI apps: chatGPT 4, Grock, Stocksuits, Pre-market Data Summary: Write a function to pull and summarize pre-market data such as high, low, and volume: python Copy code def premarket_summary(ticker): premarket_data = api.get_barset(ticker, [login to view URL], limit=100, after="09:30:00") high = max([bar.h for bar in premarket_data[ticker]]) low = min([bar.l for bar in premarket_data[ticker]]) volume = sum([bar.v for bar in premarket_data[ticker]]) return high, low, volume Technical Indicators Calculation Camarilla Pivot Points: The AI agent should compute pivot points based on Camarilla formulas: python Copy code def calculate_camarilla_pivots(high, low, close): R4 = close + (high - low) * 1.1 / 2 R3 = close + (high - low) * 1.1 / 4 S3 = close - (high - low) * 1.1 / 4 S4 = close - (high - low) * 1.1 / 2 return R4, R3, S3, S4 Moving Averages: Add functions for calculating simple moving averages (SMA) and exponential moving averages (EMA): python Copy code def calculate_sma(data, window): return [login to view URL](window=window).mean() def calculate_ema(data, window): return [login to view URL](span=window, adjust=False).mean() Backtesting Test I agent's performance using historical data to validate its effectiveness before deploying it in real trading. AI/ML Model Development Feature Engineering: Create features from technical indicators, sentiment analysis, or volume: python Copy code def create_features(data): data['SMA_50'] = calculate_sma(data['close'], 50) data['EMA_20'] = calculate_ema(data['close'], 20) data['Volatility'] = data['high'] - data['low'] return [login to view URL]() Model Training: Utilize an ML framework like PyTorch Lightning to build a predictive model: python Copy code import pytorch_lightning as pl import [login to view URL] as nn import torch class TradingModel([login to view URL]): def __init__(self): super().__init__() [login to view URL] = [login to view URL](input_size=5, hidden_size=128, batch_first=True) [login to view URL] = [login to view URL](128, 2) # For binary classification (buy/sell) def forward(self, x): x, _ = [login to view URL](x) return [login to view URL](x[:, -1, :]) # Use the last output of LSTM for prediction def training_step(self, batch, batch_idx): inputs, labels = batch outputs = self(inputs) loss = [login to view URL]()(outputs, labels) return loss Backtesting & Simulation: Implement backtesting to evaluate the model's performance with historical data: python Copy code def backtest(data, model): for index, row in [login to view URL](): features = [login to view URL](row[features_columns].values).unsqueeze(0) prediction = model(features) # Implement logic for buy/sell based on prediction Lightning AI Workflow Building the Lightning Component: The agent can be encapsulated in a Lightning AI component, defining tasks, data processing, and trading logic: python Copy code import lightning as L from [login to view URL] import LightningWork class TradingAgent(LightningWork): def __init__(self): super().__init__() # Initialize trading logic, ML models, etc. def run(self): # Trading logic here, can use data fetched and models built earlier pass Deploying the Application: You need to combine the agent into a LightningFlow: python Copy code class TradingApp([login to view URL]): def __init__(self): super().__init__() [login to view URL] = TradingAgent() def run(self): [login to view URL]() app = [login to view URL](TradingApp()) Running on Lightning Cloud: After building and testing locally, deploy the AI agent to Lightning AI's cloud environment for real-time operation: bash Copy code lightning run app [login to view URL] Integration with Broker’s API for Live Trading Connect to Broker API: Integrate the AI agent with a trading broker, such as Alpaca or IBKR, for executing trades: python Copy code api.submit_order( symbol='AAPL', qty=1, side='buy', type='market', time_in_force='gtc', ) Monitoring & Optimization Continuously monitor the AI agent's performance and retrain it with new data to adapt to changing market conditions. Risk Management Incorporate risk management strategies, such as stop-loss orders, position sizing, and diversification, to minimize potential losses Monitor and Update: Continuously monitor the model's predictions, performance, and market conditions for dynamic adjustments. Logging and Monitoring Add Logging: Ensure the system logs trade signals, execution, and model performance for future analysis. Alert Systems: You can also set up alert systems (e.g., email, SMS) to notify about critical events during trading sessions. Feedback Loop Optimization Regularly update and optimize the agent's strategy based on feedback loops from its performance. For instance, the agent could track its success rate with different setups and adjust its strategy dynamically over time, effectively learning from past trades. This could involve a reinforcement learning layer, where the agent gradually refines its strategy with real-time feedback from the market. Summary for the Python Developer: Install the necessary dependencies and set up the Lightning AI platform. Fetch pre-market and live data using APIs like Alpaca or Yahoo Finance. Compute technical indicators (e.g., Camarilla Pivot Points, moving averages) and preprocess the data. Train machine learning models using pytorch-lightning to predict trade actions. Integrate with a broker API for executing trades based on the model's predictions. Deploy the AI agent to Lightning AI's cloud for real-time trading and continuous improvement.