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MIDAS TECHNOLOGIES: Executive Summary Mission Statement: The goal of Midas Technologies is to develop algorithmic investment software over-time to build a diversified fund of algorithmic trading strategies that yield an above market return on as frequent a basis as possible.
Business Model: Build an algorithmic trading Python program, that accurately predicts, and trades shares and options on the price of crude oil. This algorithm will have the goal of getting a given return with a given risk level on a weekly basis. The algorithm will not be used on the market until it has a consistent win rate of 60% and above. We will trade this based on technical indicators
Technology Overview: The program will be built like this: Price Prediction Models: Speculative Indicators: These will be functions that analyze aspects of a news article or other variable to come to a conclusion of how much the price of oil will change in a dollar value. Each indicator needs to print out a predicted price for tomorrow Economic Indicators: These will be functions that analyze relationships between different economic factors based on data such as GDP, supply, demand, and USD. Each indicator need to print out a predicted price for tomorrow All prices will be fed into a weighted average formula. The formula will show the predicted price at market close tomorrow. The weights represent the percentage the market thinks that particular indicator is important PriceTomorrow= PriceNews〖(w〗_1)PriceSupply(w_2 )PriceDemand(w_3 )…. Market Importance Ranking Based on optimizing of the above problem to minimize margin of error, we will find the weights. These weights will be the backbone of our predictions and we must test them continuously to find the most accurate price prediction.
Trading Bot
MIDAS TECHNOLOGIES: Roles and Responsibilities Board of Directors: Jacob Mardian: 1 Vote Equity: 33.33% Role: Responsibilities: Business paperwork, research, learn more about trading strategies, code trading bot Griffin Witt: 1 Vote Equity: 33.33% Role: Chief of Responsibilities: Building the intrinsic evaluation system. Finding the relationships between different economic and measurable factors to determine a price Collin Schaufele: 1 Vote Equity: 33.33% Role: Chief of Systems Responsibilities: Building speculation model that finds an estimated price based on as many possible speciulative indicators as possible and get a license
MIDAS TECHNOLOGIES: Technology Overview Stage 1: Architecture and Modularity Core Architectural Design The first step is to refactor the existing code into distinct, modular components. Each component should be as independent as possible, allowing flexibility and scalability for future enhancements. Modularization Plan: Data Acquisition Module: Handles all API calls to get historical stock, options, and other relevant market data. Signal Generation Module: Incorporates all technical indicators (e.g., Moving Average, RSI, Bollinger Bands, etc.) and allows plug-and-play functionality for new strategies. Optimization Module: Responsible for finding the best combination of strategy weights using an optimization function. Backtesting Module: Takes historical data, applies the strategy, and returns metrics like profit/loss, Sharpe ratio, and win rate. Risk Management Module: Includes position sizing, drawdown limits, and hedging capabilities. Execution Module: Prepares the system for live trading by handling broker API integration and managing trade execution based on generated signals. Reporting Module: Generates detailed reports (PDF, Excel, or HTML) after backtesting or live trading sessions. Object-Oriented Design (OOP) Each module will be encapsulated in an object, simplifying code management and enhancing scalability. Example: Data Acquisition Class python Copy code class DataAcquisition: def init(self, ticker): self.ticker = ticker
def fetch_price_data(self, start_date, end_date):
"""Fetch historical price data"""
data = yf.download(self.ticker, start=start_date, end=end_date)
return data
def fetch_options_chain(self, expiration_date):
"""Fetch options chain for the given expiration date"""
stock = yf.Ticker(self.ticker)
options_chain = stock.option_chain(expiration_date)
return options_chain
Stage 2: Data Acquisition and Expansion API Access and Data Sources At hedge-fund complexity, robust and comprehensive data is required. This includes expanding beyond Yahoo Finance: Yahoo Finance for initial data. IEX Cloud, Alpha Vantage, or Quandl for high-frequency data. OptionMetrics, Quandl, or CBOE for comprehensive options data. News Sentiment Data from NewsAPI or scrapers for financial sentiment from Twitter, Reddit, etc. Alternative Data Sources Advanced hedge funds use alternative data: Satellite imagery for crude oil supply insights. Social sentiment analysis via Twitter and Reddit. Macro data (inflation, crude oil futures, interest rates). Data Normalization and Preprocessing Preprocessing is critical for handling missing values and ensuring consistency across different data sources. Example of Data Preprocessing: python Copy code def preprocess_data(data): data.fillna(method='ffill', inplace=True) data['returns'] = data['Close'].pct_change() # Calculate daily returns return data
Stage 3: Model Development and Complexity Expansion Advanced Technical Indicators and Strategies To expand on current strategies, multiple timeframes and additional indicators should be added: Multi-Timeframe Strategies: Daily, weekly, and monthly analysis. Additional Indicators: MACD, Stochastic Oscillator, ADX, Ichimoku Cloud, Fibonacci Retracement, etc. Multi-Strategy Optimization: Machine learning can be used to optimize the weight of different strategies. Machine Learning for Signal Prediction Machine learning should be incorporated to improve signal predictions and strategy selection: Random Forest or XGBoost: Predict buy/sell signals based on historical data. Reinforcement Learning: To optimize decision-making based on maximizing profits. LSTM (Long Short-Term Memory): For time-series forecasting. Example: python Copy code from sklearn.ensemble import RandomForestClassifier
def train_model(data): X = data'MA', 'RSI', 'Bollinger_Bands' # Features y = data['buy_sell_signal'] # Target labels model = RandomForestClassifier(n_estimators=100) model.fit(X, y) return model Factor-Based Models Incorporate factor models frequently used in hedge funds: Momentum: Track stocks with positive returns over specific periods. Value: Use P/E ratios, EBITDA, etc., to find undervalued stocks. Volatility: Leverage implied and historical volatility as factors for decision-making.
Stage 4: Risk Management and Hedging Advanced Risk Management Incorporating sophisticated risk management is critical: Position Sizing: Allocate positions based on volatility and confidence levels. Drawdown Limits: Stop trading when predefined drawdown limits are hit. Dynamic Stop-Loss and Take-Profit: Adjust based on trailing volatility and other market conditions. Portfolio Hedging Hedge portfolios using more advanced strategies: Hedge Long/Short Positions: Use correlated assets (e.g., oil futures) to hedge directional bets. Options Spreads: Implement complex options strategies (Iron Condors, Bull Call Spreads). Beta-Hedging: Adjust based on GUSH's beta against broader market indices.
Stage 5: Scalability and Live Trading Infrastructure Execution Module Real-time trade execution and integration with broker APIs is key for scalability: Interactive Brokers API for live trade execution. Alpaca API for commission-free trading automation. Execution Risk Management: Incorporate slippage and market impact analysis. Scalable Infrastructure (Cloud-Based) Building the system on a scalable cloud infrastructure (e.g., AWS or Google Cloud) ensures high availability: Auto-Scaling: Increase computing resources during heavy data processing or trading. Fault Tolerance: Implement fail-safe mechanisms to handle unexpected system failures. Advanced Monitoring and Reporting Use real-time dashboards for strategy monitoring and exploratory analysis: Plotly Dash: Interactive dashboard for strategy performance. Jupyter Notebooks: For in-depth analysis and rapid prototyping. Real-Time Alerts Real-time notifications for market conditions or trading signals can be achieved via: SMS/Email Alerts: Integrate with Twilio or SendGrid. Webhook Integration: Slack or Telegram for team-based alerts.
Step-by-Step to Hedge-Fund Level Complexity Refactor Code into Modules: Organize code into modular components (data acquisition, strategy generation, backtesting, etc.). Upgrade Data Sources: Use multiple sources, including alternative and real-time data for better accuracy. Introduce Machine Learning: Use Random Forests, LSTMs, and Reinforcement Learning for better prediction of buy/sell signals. Risk Management: Implement advanced position sizing, drawdown limits, stop-losses, and hedging strategies. Optimize Execution: Integrate broker APIs for live trading and build a cloud-based infrastructure for scalability. Add Hedging Capabilities: Implement multi-leg options strategies, beta-hedging, and portfolio risk management. Monitor and Improve: Create real-time dashboards, backtest rigorously, and set up alert systems.
By following this roadmap, you can evolve your trading program to hedge-fund-level complexity, adding technical sophistication, robust data acquisition, advanced machine learning, and scalable infrastructure.