118 lines
5.7 KiB
Markdown
118 lines
5.7 KiB
Markdown
# 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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