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Oil Oracle 1.0 Technology Overview
Table of Contents
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.
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.
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 |
This approach will lead to a robust, modular trading system combining real-time data processing, machine learning, and strategic decision-making capabilities.