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Add missing agent documentation from upstream
Added documentation for 18 agents: - chartanalysis_agent, chat_agent, compliance_agent - copybot_agent, funding_agent, liquidation_agent - million_agent, openrouter, realtime_clips_agent - research_agent, risk_agent, sentiment_agent - sniper_agent, solana_agent, strategy_agent - swarm_agent, trading_agent, tweet_agent This completes the documentation coverage for all agents.
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‎docs/chartanalysis_agent.md‎

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# Chart Analysis Agent
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Technical analysis using indicators and patterns.
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## What It Does
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- Calculates 20+ technical indicators
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- Identifies chart patterns (triangles, flags, etc.)
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- Detects support/resistance levels
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- Generates confluence scores
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## Usage
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```bash
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python src/agents/chartanalysis_agent.py
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```
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## Indicators Used
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- Moving Averages (SMA, EMA, VWAP)
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- Momentum (RSI, MACD, Stochastic)
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- Volatility (Bollinger Bands, ATR)
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- Volume (OBV, Volume Profile)
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## Pattern Detection
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- Head and shoulders
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- Cup and handle
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- Ascending/descending triangles
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- Bull/bear flags
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## Configuration
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```python
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CHART_TIMEFRAMES = ['5m', '1H', '4H', '1D']
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MIN_CONFLUENCE_SCORE = 3 # Min indicators agreeing
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```
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## Output
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`src/data/chartanalysis_agent/[date]/technical_analysis.json`

‎docs/chat_agent.md‎

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# Chat Agent
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Interactive AI assistant for market analysis via chat.
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## What It Does
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- Answers market questions
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- Analyzes tokens on demand
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- Provides trade recommendations
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- Explains market conditions
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## Usage
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```bash
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python src/agents/chat_agent.py
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```
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## Commands
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- `analyze [token]` - Full token analysis
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- `price [token]` - Quick price check
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- `compare [token1] [token2]` - Compare tokens
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- `market` - Overall market status
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- `/quit` - Exit
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## Features
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- Remembers conversation context
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- Can execute trades (with confirmation)
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- Pulls live data from APIs
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- Supports multiple AI models
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## Configuration
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```python
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CHAT_MODEL_TYPE = 'claude'
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CHAT_ALLOW_TRADING = False # Set True to enable trades
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```
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## Output
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Chat history saved to `src/data/chat_agent/[date]/`

‎docs/compliance_agent.md‎

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# Compliance Agent
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🌙 Moon Dev's Ad Compliance Analyzer for Facebook Advertising Guidelines
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## What It Does
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- Analyzes TikTok ads for Facebook advertising compliance
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- Extracts frames from video ads for visual analysis
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- Transcribes audio using Whisper for text analysis
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- Checks against Facebook's advertising guidelines
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- Provides compliance ratings and recommendations
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## Usage
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```bash
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python src/agents/compliance_agent.py
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```
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## Key Features
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- **Frame Extraction**: Pulls key frames from video ads
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- **Audio Transcription**: Converts speech to text for analysis
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- **AI Analysis**: Uses GPT-4o-mini to check compliance
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- **Detailed Reports**: Generates compliance scores and recommendations
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## Compliance Checks
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- Personal attributes (no assumptions about viewers)
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- Sensational content (shocking/violent imagery)
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- Adult content or nudity
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- Misleading claims or false information
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- Health claims without disclaimers
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- Before/after images compliance
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- Targeting sensitive categories
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- Prohibited products/services
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- Text-to-image ratio issues
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## Configuration
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```python
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MODEL_CONFIG = {
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"type": "openai",
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"name": "gpt-4o-mini",
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"reasoning_effort": "high"
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}
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# Video source directory
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VIDEOS_DIR = Path("/Users/md/Dropbox/dev/github/search-arbitrage/bots/compliance/tiktok_ads")
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```
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## Output Structure
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```
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src/data/compliance/
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├── analysis/
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│ ├── frames/ # Extracted video frames
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│ ├── transcripts/ # Audio transcriptions
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│ └── reports/ # Compliance analysis reports
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└── fb_guidelines.txt # Facebook guidelines reference
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```
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## Report Format
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Each analysis includes:
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- **Compliance Status**: compliant/non-compliant
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- **Overall Assessment**: Brief summary
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- **Compliance Score**: 0-100% rating
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- **Specific Issues**: Detailed violations found
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- **Recommendations**: How to fix compliance issues
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## Example Output
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```json
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{
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"compliance_status": "non-compliant",
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"overall_assessment": "Ad contains personal attribute assumptions",
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"compliance_score": 65,
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"issues": [
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"Assumes viewer characteristics",
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"Text-to-image ratio too high"
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],
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"recommendations": [
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"Remove 'You must be...' language",
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"Reduce text overlay on images"
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],
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"moon_dev_message": "Moon Dev's magic found some issues to fix! 🌙"
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}
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```
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## Use Cases
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- Pre-screen TikTok ads before Facebook submission
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- Bulk analyze ad campaigns for compliance
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- Identify common compliance issues in ad creative
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- Generate compliance reports for clients
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## Notes
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- Designed for search arbitrage ad campaigns
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- Works with TikTok ads being repurposed for Facebook
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- Helps avoid ad rejections and account issues
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- Part of Moon Dev's advertising automation suite

‎docs/copybot_agent.md‎

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# CopyBot Agent
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Copies trades from profitable wallets.
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## What It Does
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- Monitors high-performance wallets
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- Copies their trades in real-time
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- Filters by wallet win rate
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- Implements position sizing rules
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## Usage
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```bash
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python src/agents/copybot_agent.py
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```
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## Wallet Selection
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- Min 60% win rate over 30 days
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- Min 50 trades history
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- Profit >$10k
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- Active in last 24h
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## Copy Settings
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```python
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COPYBOT_MAX_WALLETS = 5 # Follow up to 5
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COPYBOT_POSITION_MULTIPLIER = 0.1 # Use 10% of their size
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COPYBOT_DELAY_SECONDS = 5 # Wait 5s after their trade
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```
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## Risk Management
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- Max $500 per copied trade
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- Skip if token in excluded list
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- Stop copying after 3 losses
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## Data Source
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Moon Dev API: `/api/copybot-follow-list`
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## Output
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`src/data/copybot_agent/[date]/copied_trades.csv`

‎docs/funding_agent.md‎

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# Funding Agent
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Monitors perpetual funding rates across exchanges.
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## What It Does
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- Tracks funding rates on Hyperliquid, Drift, etc.
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- Identifies funding arbitrage opportunities
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- Detects extreme funding as reversal signals
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- Monitors open interest changes
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## Usage
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```bash
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python src/agents/funding_agent.py
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```
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## Key Signals
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- Funding >0.1% = Overleveraged longs
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- Funding <-0.05% = Overleveraged shorts
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- Funding flips = Potential trend change
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- OI spike + funding = Squeeze setup
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## Configuration
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```python
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FUNDING_EXTREME_THRESHOLD = 0.1 # % per 8 hours
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FUNDING_ARB_MINIMUM = 0.05 # Min spread to trade
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```
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## Data Source
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Moon Dev API: `/api/funding-data`
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## Output
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`src/data/funding_agent/[date]/funding_rates.csv`

‎docs/liquidation_agent.md‎

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# Liquidation Agent
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Tracks leveraged position liquidations in real-time.
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## What It Does
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- Monitors liquidation levels across perp exchanges
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- Identifies cascade liquidation zones
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- Predicts stop-loss hunting moves
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- Alerts on unusual liquidation spikes
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## Usage
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```bash
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python src/agents/liquidation_agent.py
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```
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## Key Metrics
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- 24h liquidation volume
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- Long vs short liquidation ratio
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- Liquidation heatmap by price level
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- Largest single liquidations
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## Trading Signals
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- Mass liquidations = Local bottom/top
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- One-sided liquidations = Continuation
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- No liquidations = Low volatility ahead
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## Configuration
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```python
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LIQUIDATION_SPIKE_THRESHOLD = 10000000 # $10M in 1hr
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```
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## Data Source
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Moon Dev API: `/api/liquidation-data`
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## Output
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`src/data/liquidation_agent/[date]/liquidations.json`

‎docs/million_agent.md‎

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# Million Agent
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Focused on finding tokens with 100x-1000x potential.
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## What It Does
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- Scans for micro-cap gems
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- Analyzes tokenomics for moon potential
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- Checks early holder distribution
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- Identifies narrative catalysts
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## Usage
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```bash
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python src/agents/million_agent.py
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```
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## Selection Criteria
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- Market cap <$100k
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- Liquidity >$10k
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- Clean contract (no honeypot)
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- Strong community forming
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- Unique narrative/utility
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## Risk Management
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- Max $50 per moonshot
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- Max 10 positions
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- Auto-sell 50% at 10x
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- Let rest ride to 100x+
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## Configuration
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```python
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MILLION_MAX_MCAP = 100000
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MILLION_MAX_POSITION = 50
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MILLION_TAKE_PROFIT_1 = 10 # 10x
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```
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## Output
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`src/data/million_agent/[date]/moonshot_picks.json`

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