# Oil Oracle 1.0 Technology Overview

## Table of Contents
1. [Overview](#overview)
2. [Step-by-Step Development](#step-by-step-development)
    - [Step 1: News Scraper and Sentiment Analysis](#step-1-news-scraper-and-sentiment-analysis)
    - [Step 2: Confidence Scoring Module](#step-2-confidence-scoring-module)
    - [Step 3: Pre-Market Volatility Assessment Module](#step-3-pre-market-volatility-assessment-module)
    - [Step 4: Historical Pattern Matching](#step-4-historical-pattern-matching)
    - [Step 5: Technical Confirmation through Chart Analysis](#step-5-technical-confirmation-through-chart-analysis)
    - [Step 6: Trade Execution Decision Module](#step-6-trade-execution-decision-module)
    - [Step 7: Trade Monitoring and Exit Strategy](#step-7-trade-monitoring-and-exit-strategy)
3. [Implementation Timeline](#implementation-timeline)

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## Overview

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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## Step-by-Step Development

### Step 1: News Scraper and Sentiment Analysis

**Objective**: Scrape and analyze relevant oil news to determine market sentiment at 9:29 a.m. EST daily.

- **Source Selection**: Choose reliable oil news sources (e.g., Bloomberg, Reuters, OilPrice.com).
- **Scraping**:
  - Schedule a web scraper to pull relevant articles daily.
  - Include robust error handling, using proxies and custom user agents to prevent blocking.
- **Sentiment Analysis**:
  - **Text Preprocessing**: Clean and standardize text data by removing HTML tags, punctuation, and irrelevant symbols.
  - **NLP Model**: Use a model like BERT to determine sentiment.
    - Assign -1 for positive and +1 for negative sentiment based on expected price movements.
  - **Confidence Score**: Output a decimal confidence score to reflect sentiment strength (e.g., -0.8 for strong positive, +0.4 for moderate negative).
- **Backtesting**: Validate the model by testing historical oil-related news and price trends.

### Step 2: Confidence Scoring Module

**Objective**: Assign confidence scores to sentiment analysis predictions.

- **Algorithm**:
  - Use ensemble learning, averaging predictions across multiple NLP models.
  - Factor in the reliability of news sources and historical impact on oil prices.
- **Quality Control**:
  - Filter out low-confidence predictions below a threshold (e.g., 80%) to reduce false signals.

### Step 3: Pre-Market Volatility Assessment Module

**Objective**: Assess potential price movement based on historical patterns and pre-market data.

- **Volatility Analysis**:
  - Use volatility indicators like Average True Range (ATR) and options data for implied volatility.
  - Backtest historical price reactions to similar news events.
- **Predictive Model**:
  - Employ machine learning models (e.g., LSTM, XGBoost) to estimate daily volatility using news strength, previous day’s price action, and economic indicators.
  - **Output**: Predict intraday price movement in percentage terms to inform profit targets and stop-loss levels.

### Step 4: Historical Pattern Matching

**Objective**: Validate analysis by comparing current sentiment and volatility to historical data.

- **Pattern Matching**:
  - Build a repository of similar historical news events and their impact on oil prices.
  - Use clustering algorithms to match patterns and assign a correlation score.
- **Thresholds**: Set a minimum correlation requirement to validate trading signals.

### Step 5: Technical Confirmation through Chart Analysis

**Objective**: Align technical analysis with sentiment and volatility insights.

- **Technical Analysis**:
  - Incorporate indicators like Moving Averages, RSI, and Bollinger Bands, and analyze support/resistance levels.
  - Set confirmation rules (e.g., positive sentiment aligns with a support bounce).
- **Confirmation Logic**:
  - Only proceed with trades if technical indicators align with sentiment (e.g., RSI reversal confirming bullish sentiment).

### Step 6: Trade Execution Decision Module

**Objective**: Use combined insights to make trade decisions and select options contracts.

- **Price Projection**: Combine sentiment, volatility, historical patterns, and technical confirmation.
- **Options Selection**:
  - Assess risk and profitability using criteria like delta, theta, strike price, and expiration.
- **Risk Management**:
  - Set dynamic stop-loss and take-profit levels based on volatility predictions.

### Step 7: Trade Monitoring and Exit Strategy

**Objective**: Monitor ongoing trades and exit based on real-time market conditions.

- **Monitoring**:
  - Track real-time sentiment, price movements, and technical indicators.
  - Set alerts for mid-day sentiment changes or volatility spikes.
- **Exit Criteria**:
  - Profit Target: Sell at a pre-defined profit percentage.
  - Stop Loss: Exit if a maximum loss threshold is met.
  - Trend Reversal: Adjust or exit positions if technical indicators show reversals.

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## Implementation Timeline

| Week | Task |
|------|------|
| Weeks 1-2 | Develop news scraper and sentiment analysis system |
| Weeks 3-4 | Implement confidence scoring and pre-market volatility module |
| Weeks 5-6 | Develop historical pattern matching and technical confirmation modules |
| Weeks 7-8 | Build decision-making and trade execution modules |
| Weeks 9-10 | Integrate monitoring, alerts, and real-time adjustments |

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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.