MidasDocs/BusinessDocumentation/BusinessPlans/ExecutiveSummary.md

MIDAS TECHNOLOGIES: Executive Summary

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Table of Contents

1. Mission Statement
2. Business Model
3. Technology Overview
   • Price Prediction Models
   • Market Importance Ranking
4. Roles and Responsibilities
5. Comprehensive Technology Roadmap
   • Stage 1: Architecture and Modularity
   • Stage 2: Data Acquisition and Expansion
   • Stage 3: Model Development and Complexity Expansion
   • Stage 4: Risk Management and Hedging
   • Stage 5: Scalability and Live Trading Infrastructure
6. Implementation Pathway

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Mission Statement

The mission of Midas Technologies is to develop algorithmic investment software designed to continuously build a diversified portfolio of algorithmic trading strategies, delivering above-market returns on a consistent basis.

Business Model

Our initial product will be an algorithmic trading system focused on predicting and trading the price of crude oil. This Python-based algorithm is engineered to meet specific weekly return and risk benchmarks, using a combination of technical indicators and market sentiment.

Core Requirements:

• The algorithm will only be utilized for live trading once it consistently achieves a 60% win rate or higher.
• All trades are informed by a robust analysis of technical indicators and proprietary sentiment metrics.

Technology Overview

Price Prediction Models

1. Speculative Indicators: Functions that analyze speculative variables, such as news articles, and forecast oil price shifts based on sentiment.

   • Objective: Each indicator outputs a dollar-based price prediction for the following day.

2. Economic Indicators: Functions analyzing macroeconomic relationships, including GDP, supply, demand, and currency fluctuations.

   • Objective: Each indicator provides a forecasted price for the next trading day based on economic trends.

3. Weighted Price Prediction Formula:

   • The model will consolidate individual indicator predictions into a weighted average to produce an overall prediction.
   • Each indicator’s weight represents its market relevance, with weights optimized to minimize prediction error.
   • Formula:

     PriceTomorrow = PriceNews * (w1) + PriceSupply * (w2) + PriceDemand * (w3) + ...
     

   • These weights, continuously refined through backtesting, are foundational to the accuracy of our predictions.

Market Importance Ranking

• Our system will use optimization algorithms to dynamically adjust indicator weights, ensuring accuracy and adapting to market conditions.

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Roles and Responsibilities

Board of Directors

• Jacob Mardian

  • Equity: 33.33%
  • Role: Business Operations
  • Responsibilities: Business paperwork, research, trading strategy development, coding the trading bot.

• Griffin Witt

  • Equity: 33.33%
  • Role: Chief of Economic Analysis
  • Responsibilities: Building the intrinsic valuation system, identifying relationships among economic indicators to forecast oil prices.

• Collin Schaufele

  • Equity: 33.33%
  • Role: Chief of Speculative Analysis
  • Responsibilities: Developing models to estimate oil prices based on speculative indicators, licensing and compliance.

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Comprehensive Technology Roadmap

This roadmap outlines a progressive pathway for developing Midas Technologies’ trading platform, expanding from a basic algorithm to a hedge-fund-grade system.

Stage 1: Architecture and Modularity

1. Core Design: Begin by modularizing existing code, creating independent components for scalability and flexibility.

2. Modularization Plan:

   • Data Acquisition Module: API integration for historical and real-time market data.
   • Signal Generation Module: Incorporates technical indicators (e.g., Moving Average, RSI) for easy strategy updates.
   • Optimization Module: Finds optimal strategy weights for maximum performance.
   • Backtesting Module: Analyzes historical data, providing profit/loss, Sharpe ratio, and win rate metrics.
   • Risk Management Module: Manages position sizing, drawdown limits, and hedging.
   • Execution Module: Handles broker integration and trade execution.
   • Reporting Module: Generates detailed reports in PDF, Excel, or HTML formats post-backtesting or trading.

   Example (Python):

   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
   

Stage 2: Data Acquisition and Expansion

1. Data Sources:

   • Yahoo Finance: Initial data source.
   • IEX Cloud, Alpha Vantage: High-frequency trading data.
   • Quandl, CBOE: Options and market sentiment data.
   • Alternative Data: Social sentiment, satellite data for supply analysis.

2. Data Preprocessing: Handle missing values and normalize across data sources.

   Example:

   def preprocess_data(data):
       data.fillna(method='ffill', inplace=True)
       data['returns'] = data['Close'].pct_change()
       return data
   

Stage 3: Model Development and Complexity Expansion

1. Advanced Technical Indicators:

   • Integrate multi-timeframe analysis (daily, weekly, monthly).
   • Use advanced indicators like MACD, ADX, and Fibonacci Retracement.

2. Machine Learning for Signal Prediction:

   • Random Forests and Reinforcement Learning to enhance signal prediction.

   Example (Random Forest):

   from sklearn.ensemble import RandomForestClassifier
   def train_model(data):
       X = data[['MA', 'RSI', 'Bollinger_Bands']]
       y = data['buy_sell_signal']
       model = RandomForestClassifier(n_estimators=100)
       model.fit(X, y)
       return model
   

Stage 4: Risk Management and Hedging

1. Risk Controls:

   • Position sizing based on volatility and drawdown limits.
   • Dynamic stop-loss and take-profit settings.

2. Hedging Strategies:

   • Long/short position hedging using oil futures.
   • Options strategies like Iron Condors and Bull Call Spreads.

Stage 5: Scalability and Live Trading Infrastructure

1. Execution Module:

   • Real-time broker API integration (Interactive Brokers, Alpaca).
   • Manage execution risks like slippage.

2. Cloud-Based Scalability:

   • Deploy on AWS or Google Cloud for scalability.
   • Use auto-scaling for intensive data processing.

3. Advanced Monitoring:

   • Real-time dashboards using Plotly Dash.
   • SMS/email alerts for key trading signals.

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Implementation Pathway

Stage	Timeline	Key Tasks
Weeks 1-2	Develop Scraper & Sentiment Analysis	Build news scraper, implement sentiment analysis models.
Weeks 3-4	Confidence Scoring, Volatility Module	Add confidence scoring, build pre-market volatility prediction models.
Weeks 5-6	Historical Pattern & Technical Analysis	Implement historical pattern matching, integrate technical indicators for analysis confirmation.
Weeks 7-8	Trade Execution and Decision Modules	Develop modules for trade execution, options selection, and risk management.
Weeks 9-10	Monitoring and Real-Time Adjustments	Real-time tracking, set up alert systems, finalize dashboards.

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Through this phased approach, Midas Technologies will evolve its algorithmic trading platform to a sophisticated system with robust data processing, advanced modeling, and real-time trading capabilities.