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docs/BusinessDocumentation/BusinessPlans/ExecutiveSummary.md
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# MIDAS TECHNOLOGIES: Executive Summary
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---
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## Table of Contents
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1. [Mission Statement](#mission-statement)
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2. [Business Model](#business-model)
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3. [Technology Overview](#technology-overview)
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- [Price Prediction Models](#price-prediction-models)
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- [Market Importance Ranking](#market-importance-ranking)
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4. [Roles and Responsibilities](#roles-and-responsibilities)
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5. [Comprehensive Technology Roadmap](#comprehensive-technology-roadmap)
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- [Stage 1: Architecture and Modularity](#stage-1-architecture-and-modularity)
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- [Stage 2: Data Acquisition and Expansion](#stage-2-data-acquisition-and-expansion)
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- [Stage 3: Model Development and Complexity Expansion](#stage-3-model-development-and-complexity-expansion)
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- [Stage 4: Risk Management and Hedging](#stage-4-risk-management-and-hedging)
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- [Stage 5: Scalability and Live Trading Infrastructure](#stage-5-scalability-and-live-trading-infrastructure)
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6. [Implementation Pathway](#implementation-pathway)
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---
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## Mission Statement
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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.
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## Business Model
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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.
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**Core Requirements**:
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- The algorithm will only be utilized for live trading once it consistently achieves a **60% win rate or higher**.
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- All trades are informed by a robust analysis of technical indicators and proprietary sentiment metrics.
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## Technology Overview
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### Price Prediction Models
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1. **Speculative Indicators**: Functions that analyze speculative variables, such as news articles, and forecast oil price shifts based on sentiment.
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- **Objective**: Each indicator outputs a dollar-based price prediction for the following day.
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2. **Economic Indicators**: Functions analyzing macroeconomic relationships, including GDP, supply, demand, and currency fluctuations.
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- **Objective**: Each indicator provides a forecasted price for the next trading day based on economic trends.
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3. **Weighted Price Prediction Formula**:
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- The model will consolidate individual indicator predictions into a weighted average to produce an overall prediction.
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- Each indicator’s weight represents its market relevance, with weights optimized to minimize prediction error.
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- **Formula**:
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```
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PriceTomorrow = PriceNews * (w1) + PriceSupply * (w2) + PriceDemand * (w3) + ...
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```
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- These weights, continuously refined through backtesting, are foundational to the accuracy of our predictions.
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### Market Importance Ranking
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- Our system will use optimization algorithms to dynamically adjust indicator weights, ensuring accuracy and adapting to market conditions.
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---
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## Roles and Responsibilities
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### Board of Directors
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- **Jacob Mardian**
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- **Equity**: 33.33%
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- **Role**: Business Operations
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- **Responsibilities**: Business paperwork, research, trading strategy development, coding the trading bot.
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- **Griffin Witt**
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- **Equity**: 33.33%
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- **Role**: Chief of Economic Analysis
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- **Responsibilities**: Building the intrinsic valuation system, identifying relationships among economic indicators to forecast oil prices.
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- **Collin Schaufele**
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- **Equity**: 33.33%
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- **Role**: Chief of Speculative Analysis
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- **Responsibilities**: Developing models to estimate oil prices based on speculative indicators, licensing and compliance.
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---
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## Comprehensive Technology Roadmap
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This roadmap outlines a progressive pathway for developing Midas Technologies’ trading platform, expanding from a basic algorithm to a hedge-fund-grade system.
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### Stage 1: Architecture and Modularity
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1. **Core Design**: Begin by modularizing existing code, creating independent components for scalability and flexibility.
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2. **Modularization Plan**:
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- **Data Acquisition Module**: API integration for historical and real-time market data.
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- **Signal Generation Module**: Incorporates technical indicators (e.g., Moving Average, RSI) for easy strategy updates.
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- **Optimization Module**: Finds optimal strategy weights for maximum performance.
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- **Backtesting Module**: Analyzes historical data, providing profit/loss, Sharpe ratio, and win rate metrics.
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- **Risk Management Module**: Manages position sizing, drawdown limits, and hedging.
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- **Execution Module**: Handles broker integration and trade execution.
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- **Reporting Module**: Generates detailed reports in PDF, Excel, or HTML formats post-backtesting or trading.
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**Example (Python)**:
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```python
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class DataAcquisition:
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def __init__(self, ticker):
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self.ticker = ticker
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def fetch_price_data(self, start_date, end_date):
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"""Fetch historical price data"""
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data = yf.download(self.ticker, start=start_date, end=end_date)
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return data
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```
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### Stage 2: Data Acquisition and Expansion
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1. **Data Sources**:
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- **Yahoo Finance**: Initial data source.
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- **IEX Cloud, Alpha Vantage**: High-frequency trading data.
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- **Quandl, CBOE**: Options and market sentiment data.
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- **Alternative Data**: Social sentiment, satellite data for supply analysis.
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2. **Data Preprocessing**: Handle missing values and normalize across data sources.
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**Example**:
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```python
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def preprocess_data(data):
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data.fillna(method='ffill', inplace=True)
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data['returns'] = data['Close'].pct_change()
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return data
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```
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### Stage 3: Model Development and Complexity Expansion
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1. **Advanced Technical Indicators**:
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- Integrate multi-timeframe analysis (daily, weekly, monthly).
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- Use advanced indicators like MACD, ADX, and Fibonacci Retracement.
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2. **Machine Learning for Signal Prediction**:
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- **Random Forests** and **Reinforcement Learning** to enhance signal prediction.
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**Example (Random Forest)**:
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```python
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from sklearn.ensemble import RandomForestClassifier
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def train_model(data):
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X = data[['MA', 'RSI', 'Bollinger_Bands']]
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y = data['buy_sell_signal']
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model = RandomForestClassifier(n_estimators=100)
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model.fit(X, y)
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return model
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```
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### Stage 4: Risk Management and Hedging
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1. **Risk Controls**:
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- Position sizing based on volatility and drawdown limits.
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- Dynamic stop-loss and take-profit settings.
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2. **Hedging Strategies**:
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- Long/short position hedging using oil futures.
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- Options strategies like Iron Condors and Bull Call Spreads.
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### Stage 5: Scalability and Live Trading Infrastructure
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1. **Execution Module**:
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- Real-time broker API integration (Interactive Brokers, Alpaca).
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- Manage execution risks like slippage.
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2. **Cloud-Based Scalability**:
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- Deploy on AWS or Google Cloud for scalability.
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- Use auto-scaling for intensive data processing.
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3. **Advanced Monitoring**:
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- Real-time dashboards using Plotly Dash.
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- SMS/email alerts for key trading signals.
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---
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## Implementation Pathway
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| Stage | Timeline | Key Tasks |
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|---------------|-------------------------|-------------------------------------------------------------------------------------------------|
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| Weeks 1-2 | Develop Scraper & Sentiment Analysis | Build news scraper, implement sentiment analysis models. |
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| Weeks 3-4 | Confidence Scoring, Volatility Module | Add confidence scoring, build pre-market volatility prediction models. |
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| Weeks 5-6 | Historical Pattern & Technical Analysis | Implement historical pattern matching, integrate technical indicators for analysis confirmation.|
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| Weeks 7-8 | Trade Execution and Decision Modules | Develop modules for trade execution, options selection, and risk management. |
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| Weeks 9-10 | Monitoring and Real-Time Adjustments | Real-time tracking, set up alert systems, finalize dashboards. |
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---
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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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```
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docs/BusinessDocumentation/BusinessPlans/oil_oracle.md
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docs/BusinessDocumentation/BusinessPlans/oil_oracle.md
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# Oil Oracle 1.0 Technology Overview
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## Table of Contents
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1. [Overview](#overview)
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2. [Step-by-Step Development](#step-by-step-development)
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- [Step 1: News Scraper and Sentiment Analysis](#step-1-news-scraper-and-sentiment-analysis)
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- [Step 2: Confidence Scoring Module](#step-2-confidence-scoring-module)
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- [Step 3: Pre-Market Volatility Assessment Module](#step-3-pre-market-volatility-assessment-module)
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- [Step 4: Historical Pattern Matching](#step-4-historical-pattern-matching)
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- [Step 5: Technical Confirmation through Chart Analysis](#step-5-technical-confirmation-through-chart-analysis)
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- [Step 6: Trade Execution Decision Module](#step-6-trade-execution-decision-module)
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- [Step 7: Trade Monitoring and Exit Strategy](#step-7-trade-monitoring-and-exit-strategy)
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3. [Implementation Timeline](#implementation-timeline)
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---
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## Overview
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The **Oil Oracle 1.0** system is designed to analyze market sentiment, volatility, and technical patterns in real-time to make informed trading decisions. The following step-by-step guide outlines each module of the system, detailing how these components will work together to identify optimal trading opportunities.
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---
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## Step-by-Step Development
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### Step 1: News Scraper and Sentiment Analysis
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**Objective**: Scrape and analyze relevant oil news to determine market sentiment at 9:29 a.m. EST daily.
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- **Source Selection**: Choose reliable oil news sources (e.g., Bloomberg, Reuters, OilPrice.com).
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- **Scraping**:
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- Schedule a web scraper to pull relevant articles daily.
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- Include robust error handling, using proxies and custom user agents to prevent blocking.
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- **Sentiment Analysis**:
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- **Text Preprocessing**: Clean and standardize text data by removing HTML tags, punctuation, and irrelevant symbols.
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- **NLP Model**: Use a model like BERT to determine sentiment.
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- Assign -1 for positive and +1 for negative sentiment based on expected price movements.
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- **Confidence Score**: Output a decimal confidence score to reflect sentiment strength (e.g., -0.8 for strong positive, +0.4 for moderate negative).
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- **Backtesting**: Validate the model by testing historical oil-related news and price trends.
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### Step 2: Confidence Scoring Module
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**Objective**: Assign confidence scores to sentiment analysis predictions.
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- **Algorithm**:
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- Use ensemble learning, averaging predictions across multiple NLP models.
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- Factor in the reliability of news sources and historical impact on oil prices.
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- **Quality Control**:
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- Filter out low-confidence predictions below a threshold (e.g., 80%) to reduce false signals.
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### Step 3: Pre-Market Volatility Assessment Module
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**Objective**: Assess potential price movement based on historical patterns and pre-market data.
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- **Volatility Analysis**:
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- Use volatility indicators like Average True Range (ATR) and options data for implied volatility.
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- Backtest historical price reactions to similar news events.
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- **Predictive Model**:
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- Employ machine learning models (e.g., LSTM, XGBoost) to estimate daily volatility using news strength, previous day’s price action, and economic indicators.
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- **Output**: Predict intraday price movement in percentage terms to inform profit targets and stop-loss levels.
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### Step 4: Historical Pattern Matching
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**Objective**: Validate analysis by comparing current sentiment and volatility to historical data.
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- **Pattern Matching**:
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- Build a repository of similar historical news events and their impact on oil prices.
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- Use clustering algorithms to match patterns and assign a correlation score.
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- **Thresholds**: Set a minimum correlation requirement to validate trading signals.
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### Step 5: Technical Confirmation through Chart Analysis
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**Objective**: Align technical analysis with sentiment and volatility insights.
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- **Technical Analysis**:
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- Incorporate indicators like Moving Averages, RSI, and Bollinger Bands, and analyze support/resistance levels.
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- Set confirmation rules (e.g., positive sentiment aligns with a support bounce).
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- **Confirmation Logic**:
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- Only proceed with trades if technical indicators align with sentiment (e.g., RSI reversal confirming bullish sentiment).
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### Step 6: Trade Execution Decision Module
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**Objective**: Use combined insights to make trade decisions and select options contracts.
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- **Price Projection**: Combine sentiment, volatility, historical patterns, and technical confirmation.
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- **Options Selection**:
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- Assess risk and profitability using criteria like delta, theta, strike price, and expiration.
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- **Risk Management**:
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- Set dynamic stop-loss and take-profit levels based on volatility predictions.
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### Step 7: Trade Monitoring and Exit Strategy
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**Objective**: Monitor ongoing trades and exit based on real-time market conditions.
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- **Monitoring**:
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- Track real-time sentiment, price movements, and technical indicators.
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- Set alerts for mid-day sentiment changes or volatility spikes.
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- **Exit Criteria**:
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- Profit Target: Sell at a pre-defined profit percentage.
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- Stop Loss: Exit if a maximum loss threshold is met.
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- Trend Reversal: Adjust or exit positions if technical indicators show reversals.
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---
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## Implementation Timeline
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| Week | Task |
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|------|------|
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| Weeks 1-2 | Develop news scraper and sentiment analysis system |
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| Weeks 3-4 | Implement confidence scoring and pre-market volatility module |
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| Weeks 5-6 | Develop historical pattern matching and technical confirmation modules |
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| Weeks 7-8 | Build decision-making and trade execution modules |
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| Weeks 9-10 | Integrate monitoring, alerts, and real-time adjustments |
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---
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This approach will lead to a robust, modular trading system combining real-time data processing, machine learning, and strategic decision-making capabilities.
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