185 lines
8.3 KiB
Markdown
185 lines
8.3 KiB
Markdown
# 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. |
|
||
|
||
---
|
||
|
||
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.
|
||
```
|