# MIDAS TECHNOLOGIES: Executive Summary

---

## Table of Contents
1. [Mission Statement](#mission-statement)
2. [Business Model](#business-model)
3. [Technology Overview](#technology-overview)
    - [Price Prediction Models](#price-prediction-models)
    - [Market Importance Ranking](#market-importance-ranking)
4. [Roles and Responsibilities](#roles-and-responsibilities)
5. [Comprehensive Technology Roadmap](#comprehensive-technology-roadmap)
    - [Stage 1: Architecture and Modularity](#stage-1-architecture-and-modularity)
    - [Stage 2: Data Acquisition and Expansion](#stage-2-data-acquisition-and-expansion)
    - [Stage 3: Model Development and Complexity Expansion](#stage-3-model-development-and-complexity-expansion)
    - [Stage 4: Risk Management and Hedging](#stage-4-risk-management-and-hedging)
    - [Stage 5: Scalability and Live Trading Infrastructure](#stage-5-scalability-and-live-trading-infrastructure)
6. [Implementation Pathway](#implementation-pathway)

---

## 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.

---

## 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.

---

## 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)**:
   ```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**:
   ```python
   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)**:
   ```python
   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.

---

## 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.
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