* Support 3m volume and ATR4
* test(market): add unit tests for Volume and ATR14 indicators
- Add comprehensive tests for calculateIntradaySeries Volume collection
- Add tests for ATR14 calculation with various data scenarios
- Add edge case tests for insufficient data
- Test Volume value precision and consistency with other indicators
- All 8 test cases pass successfully
Resolves code review blocking issue from PR #830
## Problem
GetCurrentKlines had two critical bugs causing price data to become stale:
1. Incorrect return logic: returned error even when data fetch succeeded
2. Race condition: returned slice reference instead of deep copy, causing concurrent data corruption
## Impact
- BTC price stuck at 106xxx while actual market price was 107xxx+
- LLM calculated take-profit based on stale prices → orders failed validation
- Statistics showed incorrect P&L (0.00%) due to corrupted historical data
- Alt-coins filtered out due to failed market data fetch
## Solution
1. Fixed return logic: only return error when actual failure occurs
2. Return deep copy instead of reference to prevent race conditions
3. Downgrade subscription errors to warnings (non-blocking)
## Test Results
✅ Price updates in real-time
✅ Take-profit orders execute successfully
✅ P&L calculations accurate
✅ Alt-coins now tradeable
Related: Price feed mechanism, concurrent data access
Fixed critical bug where 4h kline data was incorrectly stored with
3m kline data, causing data mismatch.
Changes:
- Changed `m.klineDataMap4h.Store(s, klines)` to `klines4h`
- Updated log message to use `len(klines4h)` instead of `len(klines)`
This bug would cause 4h kline queries to return 3m data, leading to
incorrect technical indicator calculations.
Fixes: #260
Related: #176
Remove deprecated files that don't exist in nofx internal version:
- market/ai_decision_engine.go
- market/ai_signal.go
- market/market_data.go
- scanner/ai_scanner.go
Keep only market/data.go to align with internal version structure.
Co-Authored-By: tinkle-community <tinklefund@gmail.com>
Architecture improvements:
- Extract AI decision engine to dedicated `decision` package
- Create `mcp` package for Model Context Protocol client
- Separate market data structures into `market/data.go`
- Update trader to use new modular structure
New packages:
- `decision/engine.go` - AI decision logic and prompt building
- `mcp/client.go` - Unified AI API client (DeepSeek/Qwen)
- `market/data.go` - Market data type definitions
Benefits:
- Better separation of concerns
- Improved code organization and maintainability
- Easier to test individual components
- More flexible AI provider integration
- Cleaner dependency management
Updated imports:
- trader/auto_trader.go now uses decision and mcp packages
- Consistent API across different AI providers
Co-Authored-By: tinkle-community <tinklefund@gmail.com>
Major improvements:
- Use period-level Sharpe ratio (range -2 to +2) instead of annualized
- Save full user prompt in decision logs for debugging
- Format complete market data (3m + 4h candles) for AI analysis
- Prevent position stacking with duplicate position checks
- Update Sharpe ratio interpretation thresholds
Market data enhancements:
- Display full technical indicators in user prompt
- Include 3-minute and 4-hour timeframe data
- Add OI (Open Interest) change and funding rate signals
Risk control:
- Block opening duplicate positions (same symbol + direction)
- Suggest close action first before opening new position
- Prevent margin usage from exceeding limits
UI improvements:
- Update multi-language translations
- Refine AI learning dashboard display
Co-Authored-By: tinkle-community <tinklefund@gmail.com>
Problem: When TotalTrades == 0 (no completed trades), formatPerformanceFeedback
would return early, hiding Sharpe ratio and adaptive behavior recommendations
from AI's prompt. This caused AI to say "no historical data" even though Sharpe
ratio was calculated and displayed on frontend.
Solution:
- Display Sharpe ratio BEFORE checking TotalTrades
- Don't return early when TotalTrades == 0
- Always show adaptive behavior recommendations if Sharpe ratio exists
- Sharpe ratio is calculated from account equity changes, not just closed trades
Impact: AI can now properly use Sharpe ratio for self-evolution, even in early
stages before any trades are closed.
Co-Authored-By: tinkle-community <tinklefund@gmail.com>
- Implement Sharpe ratio calculation in decision logger
- Add adaptive behavior recommendations based on Sharpe ratio
- Display Sharpe ratio in AI learning dashboard with visual indicators
- Enable AI to adjust trading strategy based on risk-adjusted returns
- Color-coded performance levels (red/yellow/green) for easy monitoring
Co-Authored-By: tinkle-community <tinklefund@gmail.com>
- Split AI prompts into system (cacheable) and user (dynamic) messages
- Add confidence and risk_usd fields to trading decisions
- Reduce temperature to 0.5 for more stable JSON output
- Add retry mechanism for AI API calls
- Simplify candidate display (show top 10 only)
- Improve prompt structure for better AI decision quality
- Replace regex-based quote fixing with simple character replacement
- Add Twitter/X contact link to all README versions
- Remove complex regex pattern for better performance
- Fix Chinese quote mark handling in AI responses
- Add focused position strategy (max 3 concurrent positions)
- Increase position sizes for better capital efficiency
- Add historical reflection prompts for AI learning
- Fix JSON parsing with missing quotes in reasoning field
- Update position limits and leverage recommendations
- Enhance risk management with concentrated positions
- Frontend trading records and UI enhancements
- Optimized AI prompts and decision engine
- Performance analysis and comparison features
- Binance-style UI improvements
- Multi-AI competition mode (Qwen vs DeepSeek)
- Binance Futures integration
- AI self-learning mechanism
- Professional web dashboard
- Complete risk management system
Icy
0xYYBB | ZYY | Bobo
Lawrence Liu
Shui
Diego
0xYYBB | ZYY | Bobo
Icyoung
Ethan
icy
yuanshi2016
tinkle-community