mirror of
https://github.com/laoxong/nofx.git
synced 2026-06-04 09:58:22 +08:00
7e96c5d0f2
* feat: add AI grid trading and market regime classification - Add GridTrader interface with PlaceLimitOrder, CancelOrder, GetOrderBook - Implement GridTrader for all exchanges (Binance, Bybit, OKX, Bitget, Hyperliquid, Aster, Lighter) - Add grid engine with ATR-based boundary calculation and fund distribution - Add market regime classification documents (Chinese/English) - Add GridConfigEditor component for frontend configuration * fix: implement GetOpenOrders for Lighter exchange * debug: add logging for Lighter GetActiveOrders API call * fix: correct Lighter API response parsing for GetOpenOrders - Changed response field from 'data' to 'orders' to match Lighter API - Updated OrderResponse struct to match Lighter's actual field names - Fixed field types: price/quantity as strings, is_ask for side * feat: implement GetOpenOrders for Aster, OKX, Bitget exchanges - Aster: uses /fapi/v3/openOrders endpoint - OKX: uses /api/v5/trade/orders-pending and orders-algo-pending - Bitget: uses /api/v2/mix/order/orders-pending and orders-plan-pending * fix: address code review issues for GetOpenOrders - Add error logging for OKX/Bitget API failures (was silently swallowed) - Fix Lighter position side logic to handle reduce-only orders - Change verbose debug logs from Infof to Debugf level * fix: provide FromAccountIndex and ApiKeyIndex for Lighter nonce auto-fetch Root cause: SDK requires these fields to fetch nonce from API, otherwise nonce gets cached/stuck * fix: use auth query parameter instead of Authorization header for Lighter API * test: add Lighter API authentication tests and diagnostic tools * fix(grid): add leverage setting before order placement CRITICAL BUG FIX: - Call SetLeverage() in GridTraderAdapter.PlaceLimitOrder() - Set leverage during grid initialization - Log leverage setting results * fix(grid): prevent CancelOrder from canceling all orders CRITICAL BUG FIX: - CancelOrder no longer calls CancelAllOrders - Try exchange-specific CancelOrder if available - Return error if individual cancellation not supported * fix(grid): add total position value limit check CRITICAL: Prevent excessive position accumulation - New checkTotalPositionLimit() function - Checks current + pending + new order value - Rejects orders that would exceed TotalInvestment x Leverage - Logs clear error messages when limit exceeded * feat(grid): implement stop loss execution CRITICAL: Add code-level stop loss protection - New checkAndExecuteStopLoss() function - Checks each filled level against StopLossPct - Automatically closes positions exceeding stop loss - Called during every grid state sync * feat(grid): add breakout detection and auto-pause CRITICAL: Detect price breakout from grid range - New checkBreakout() function to detect upper/lower breakouts - Auto-pause grid on significant breakout (>2%) - Cancel all orders when breakout detected - Prevent continued losses in trending market - Minor breakouts (1-2%) logged for AI consideration * feat(grid): enforce max drawdown limit with emergency exit CRITICAL: Add drawdown protection - New checkMaxDrawdown() function tracks peak equity - emergencyExit() closes all positions and cancels orders - Auto-pause grid when MaxDrawdownPct exceeded - Protect capital from excessive losses * feat(grid): enforce daily loss limit - Add checkDailyLossLimit() function to check if daily loss exceeds limit - Track daily PnL with auto-reset at midnight - Pause grid when DailyLossLimitPct exceeded - Add updateDailyPnL() helper for realized PnL tracking - Prevent excessive single-day losses * fix(grid): update daily PnL when stop loss is executed The updateDailyPnL() function was added but never called, leaving DailyPnL always at 0 and preventing daily loss limit checks from triggering. This fix updates DailyPnL and TotalProfit directly in checkAndExecuteStopLoss() when a stop loss is executed. We update directly rather than calling updateDailyPnL() because the mutex is already held in that function. * feat(grid): add automatic grid adjustment - New checkGridSkew() detects imbalanced grid - autoAdjustGrid() reinitializes around current price - Prevents grid from becoming ineffective after drift - Triggers when one side is 3x more filled than other * fix(grid): recalculate bounds in autoAdjustGrid before reinitializing levels Critical fix for grid auto-adjustment: - Recalculate grid bounds (UpperPrice, LowerPrice, GridSpacing) centered on current price before reinitializing grid levels - Preserve filled positions during adjustment by saving and restoring them to the closest new level after reinitialization - Hold mutex lock for the entire adjustment operation to ensure atomicity - Add locked variants of calculateDefaultBounds, calculateATRBounds, and initializeGridLevels to use during adjustment Without this fix, autoAdjustGrid was using old boundaries when creating new grid levels, defeating the purpose of auto-adjustment when price moved significantly. * fix(grid): improve order state sync logic - Don't assume missing orders are filled - Compare position size to determine fill vs cancel - Properly reset cancelled orders to empty state - More accurate grid state tracking * fix(grid): use actual PositionSize sum instead of count in syncGridState heuristic The position-based heuristic was using `float64(previousFilledCount) * level.OrderQuantity` which incorrectly assumed uniform order quantities. Since the grid uses weighted distribution (gaussian, pyramid, uniform) where orders have different quantities, this could lead to incorrect fill detection. Now sums the actual PositionSize from filled levels for accurate comparison. Also adds warning log when GetPositions() fails. * docs: add grid market regime detection design Design for enhanced market state recognition with: - Multi-dimensional indicators (ATR, Bollinger, EMA, MACD, RSI) - Multi-period box indicators (72/240/500 1h candles) - 4-level ranging classification - Breakout detection and handling - Frontend risk control panel * docs: add grid market regime implementation plan 20 tasks covering: - Donchian channel calculation - Box data types and API - Regime classification (4 levels) - Breakout detection and handling - False breakout recovery - Frontend risk panel - AI prompt updates * feat(market): add Donchian channel calculation Add calculateDonchian function to compute highest high and lowest low over a specified period. This is the foundation for box (range) detection in the multi-period box indicator system for grid trading. * fix(market): handle invalid period in calculateDonchian * feat(market): add BoxData and RegimeLevel types * feat(market): add GetBoxData for multi-period box calculation Adds calculateBoxData internal function and GetBoxData public API that fetches 1h klines and computes three Donchian box levels (short/mid/long). This will be used by the grid trading system to detect market regime. * feat(store): add box and regime fields to grid models * feat(trader): add regime classification and breakout detection Implements Tasks 6-9 for grid market regime awareness: - Task 6: classifyRegimeLevel with Bollinger/ATR thresholds - Task 7: detectBoxBreakout for multi-period box breakouts - Task 8: confirmBreakout with 3-candle confirmation logic - Task 9: getBreakoutAction mapping breakout levels to actions * feat(trader): integrate box breakout detection into grid cycle - Task 10: Add checkBoxBreakout with 3-candle confirmation - Task 11: Add checkFalseBreakoutRecovery for 50% position recovery - Task 12: Add box/breakout/regime fields to GridState * feat: add grid risk panel with API endpoint - Task 13: Add GridRiskInfo type to frontend - Task 14: Add /traders/:id/grid-risk API endpoint - Task 15: Add GetGridRiskInfo method to AutoTrader - Task 16: Create GridRiskPanel component with i18n * feat(kernel): add box indicators to AI prompt - Add BoxData field to GridContext - Add box indicator table to both zh/en prompts - Show breakout/warning alerts based on price position * feat(web): integrate GridRiskPanel into TraderDashboardPage * feat(lighter): improve API key validation and market caching - Add API key validation status tracking - Add market list caching to reduce API calls - Improve logging (debug vs info levels) - Add comprehensive integration tests - Update trader manager and store for lighter support * fix: remove hardcoded test wallet address * fix(grid): improve GridRiskPanel layout and fix liquidation data - Make panel collapsible with summary badges when collapsed - Use compact 2-column grid layout for detailed info - Fix auth token key (token -> auth_token) - Only calculate liquidation distance when position exists * fix(grid): add isRunning checks to prevent trades after Stop() is called
586 lines
16 KiB
Go
586 lines
16 KiB
Go
package market
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import (
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"math"
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"testing"
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)
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// generateTestKlines generates test K-line data
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func generateTestKlines(count int) []Kline {
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klines := make([]Kline, count)
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for i := 0; i < count; i++ {
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// Generate simulated price data with some fluctuation
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basePrice := 100.0
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variance := float64(i%10) * 0.5
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open := basePrice + variance
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high := open + 1.0
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low := open - 0.5
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close := open + 0.3
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volume := 1000.0 + float64(i*100)
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klines[i] = Kline{
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OpenTime: int64(i * 180000), // 3-minute interval
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Open: open,
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High: high,
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Low: low,
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Close: close,
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Volume: volume,
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CloseTime: int64((i+1)*180000 - 1),
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}
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}
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return klines
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}
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// TestCalculateIntradaySeries_VolumeCollection tests Volume data collection
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func TestCalculateIntradaySeries_VolumeCollection(t *testing.T) {
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tests := []struct {
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name string
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klineCount int
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expectedVolLen int
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}{
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{
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name: "Normal case - 20 K-lines",
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klineCount: 20,
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expectedVolLen: 10, // Should collect latest 10
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},
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{
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name: "Exactly 10 K-lines",
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klineCount: 10,
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expectedVolLen: 10,
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},
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{
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name: "Less than 10 K-lines",
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klineCount: 5,
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expectedVolLen: 5, // Should return all 5
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},
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{
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name: "More than 10 K-lines",
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klineCount: 30,
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expectedVolLen: 10, // Should only return latest 10
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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klines := generateTestKlines(tt.klineCount)
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data := calculateIntradaySeries(klines)
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if data == nil {
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t.Fatal("calculateIntradaySeries returned nil")
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}
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if len(data.Volume) != tt.expectedVolLen {
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t.Errorf("Volume length = %d, want %d", len(data.Volume), tt.expectedVolLen)
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}
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// Verify Volume data correctness
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if len(data.Volume) > 0 {
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// Calculate expected start index
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start := tt.klineCount - 10
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if start < 0 {
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start = 0
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}
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// Verify first Volume value
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expectedFirstVolume := klines[start].Volume
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if data.Volume[0] != expectedFirstVolume {
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t.Errorf("First volume = %.2f, want %.2f", data.Volume[0], expectedFirstVolume)
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}
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// Verify last Volume value
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expectedLastVolume := klines[tt.klineCount-1].Volume
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lastVolume := data.Volume[len(data.Volume)-1]
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if lastVolume != expectedLastVolume {
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t.Errorf("Last volume = %.2f, want %.2f", lastVolume, expectedLastVolume)
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}
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}
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})
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}
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}
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// TestCalculateIntradaySeries_VolumeValues tests Volume value correctness
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func TestCalculateIntradaySeries_VolumeValues(t *testing.T) {
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klines := []Kline{
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{Close: 100.0, Volume: 1000.0, High: 101.0, Low: 99.0, Open: 100.0},
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{Close: 101.0, Volume: 1100.0, High: 102.0, Low: 100.0, Open: 101.0},
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{Close: 102.0, Volume: 1200.0, High: 103.0, Low: 101.0, Open: 102.0},
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{Close: 103.0, Volume: 1300.0, High: 104.0, Low: 102.0, Open: 103.0},
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{Close: 104.0, Volume: 1400.0, High: 105.0, Low: 103.0, Open: 104.0},
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{Close: 105.0, Volume: 1500.0, High: 106.0, Low: 104.0, Open: 105.0},
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{Close: 106.0, Volume: 1600.0, High: 107.0, Low: 105.0, Open: 106.0},
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{Close: 107.0, Volume: 1700.0, High: 108.0, Low: 106.0, Open: 107.0},
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{Close: 108.0, Volume: 1800.0, High: 109.0, Low: 107.0, Open: 108.0},
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{Close: 109.0, Volume: 1900.0, High: 110.0, Low: 108.0, Open: 109.0},
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}
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data := calculateIntradaySeries(klines)
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expectedVolumes := []float64{1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0, 1900.0}
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if len(data.Volume) != len(expectedVolumes) {
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t.Fatalf("Volume length = %d, want %d", len(data.Volume), len(expectedVolumes))
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}
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for i, expected := range expectedVolumes {
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if data.Volume[i] != expected {
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t.Errorf("Volume[%d] = %.2f, want %.2f", i, data.Volume[i], expected)
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}
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}
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}
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// TestCalculateIntradaySeries_ATR14 tests ATR14 calculation
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func TestCalculateIntradaySeries_ATR14(t *testing.T) {
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tests := []struct {
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name string
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klineCount int
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expectZero bool
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expectNonZero bool
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}{
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{
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name: "Sufficient data - 20 K-lines",
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klineCount: 20,
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expectNonZero: true,
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},
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{
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name: "Exactly 15 K-lines (ATR14 requires at least 15)",
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klineCount: 15,
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expectNonZero: true,
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},
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{
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name: "Insufficient data - 14 K-lines",
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klineCount: 14,
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expectZero: true,
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},
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{
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name: "Insufficient data - 10 K-lines",
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klineCount: 10,
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expectZero: true,
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},
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{
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name: "Insufficient data - 5 K-lines",
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klineCount: 5,
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expectZero: true,
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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klines := generateTestKlines(tt.klineCount)
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data := calculateIntradaySeries(klines)
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if data == nil {
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t.Fatal("calculateIntradaySeries returned nil")
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}
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if tt.expectZero && data.ATR14 != 0 {
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t.Errorf("ATR14 = %.3f, expected 0 (insufficient data)", data.ATR14)
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}
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if tt.expectNonZero && data.ATR14 <= 0 {
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t.Errorf("ATR14 = %.3f, expected > 0", data.ATR14)
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}
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})
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}
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}
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// TestCalculateATR tests ATR calculation function
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func TestCalculateATR(t *testing.T) {
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tests := []struct {
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name string
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klines []Kline
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period int
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expectZero bool
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}{
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{
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name: "Normal calculation - sufficient data",
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klines: []Kline{
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{High: 102.0, Low: 100.0, Close: 101.0},
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{High: 103.0, Low: 101.0, Close: 102.0},
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{High: 104.0, Low: 102.0, Close: 103.0},
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{High: 105.0, Low: 103.0, Close: 104.0},
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{High: 106.0, Low: 104.0, Close: 105.0},
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{High: 107.0, Low: 105.0, Close: 106.0},
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{High: 108.0, Low: 106.0, Close: 107.0},
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{High: 109.0, Low: 107.0, Close: 108.0},
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{High: 110.0, Low: 108.0, Close: 109.0},
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{High: 111.0, Low: 109.0, Close: 110.0},
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{High: 112.0, Low: 110.0, Close: 111.0},
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{High: 113.0, Low: 111.0, Close: 112.0},
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{High: 114.0, Low: 112.0, Close: 113.0},
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{High: 115.0, Low: 113.0, Close: 114.0},
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{High: 116.0, Low: 114.0, Close: 115.0},
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},
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period: 14,
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expectZero: false,
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},
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{
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name: "Insufficient data - equal to period",
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klines: []Kline{
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{High: 102.0, Low: 100.0, Close: 101.0},
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{High: 103.0, Low: 101.0, Close: 102.0},
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},
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period: 2,
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expectZero: true,
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},
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{
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name: "Insufficient data - less than period",
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klines: []Kline{
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{High: 102.0, Low: 100.0, Close: 101.0},
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},
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period: 14,
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expectZero: true,
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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atr := calculateATR(tt.klines, tt.period)
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if tt.expectZero {
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if atr != 0 {
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t.Errorf("calculateATR() = %.3f, expected 0 (insufficient data)", atr)
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}
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} else {
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if atr <= 0 {
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t.Errorf("calculateATR() = %.3f, expected > 0", atr)
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}
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}
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})
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}
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}
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// TestCalculateATR_TrueRange tests ATR True Range calculation correctness
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func TestCalculateATR_TrueRange(t *testing.T) {
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// Create a simple test case, manually calculate expected ATR
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klines := []Kline{
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{High: 50.0, Low: 48.0, Close: 49.0}, // TR = 2.0
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{High: 51.0, Low: 49.0, Close: 50.0}, // TR = max(2.0, 2.0, 1.0) = 2.0
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{High: 52.0, Low: 50.0, Close: 51.0}, // TR = max(2.0, 2.0, 1.0) = 2.0
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{High: 53.0, Low: 51.0, Close: 52.0}, // TR = 2.0
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{High: 54.0, Low: 52.0, Close: 53.0}, // TR = 2.0
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}
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atr := calculateATR(klines, 3)
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// Expected calculation:
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// TR[1] = max(51-49, |51-49|, |49-49|) = 2.0
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// TR[2] = max(52-50, |52-50|, |50-50|) = 2.0
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// TR[3] = max(53-51, |53-51|, |51-51|) = 2.0
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// Initial ATR = (2.0 + 2.0 + 2.0) / 3 = 2.0
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// TR[4] = max(54-52, |54-52|, |52-52|) = 2.0
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// Smoothed ATR = (2.0*2 + 2.0) / 3 = 2.0
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expectedATR := 2.0
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tolerance := 0.01 // Allow small floating point error
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if math.Abs(atr-expectedATR) > tolerance {
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t.Errorf("calculateATR() = %.3f, want approximately %.3f", atr, expectedATR)
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}
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}
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// TestCalculateIntradaySeries_ConsistencyWithOtherIndicators tests Volume and other indicators consistency
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func TestCalculateIntradaySeries_ConsistencyWithOtherIndicators(t *testing.T) {
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klines := generateTestKlines(30)
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data := calculateIntradaySeries(klines)
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// All arrays should exist
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if data.MidPrices == nil {
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t.Error("MidPrices should not be nil")
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}
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if data.Volume == nil {
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t.Error("Volume should not be nil")
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}
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// MidPrices and Volume should have the same length (both latest 10)
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if len(data.MidPrices) != len(data.Volume) {
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t.Errorf("MidPrices length (%d) should equal Volume length (%d)",
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len(data.MidPrices), len(data.Volume))
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}
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// All Volume values should be > 0
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for i, vol := range data.Volume {
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if vol <= 0 {
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t.Errorf("Volume[%d] = %.2f, should be > 0", i, vol)
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}
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}
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}
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// TestCalculateIntradaySeries_EmptyKlines tests empty K-line data
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func TestCalculateIntradaySeries_EmptyKlines(t *testing.T) {
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klines := []Kline{}
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data := calculateIntradaySeries(klines)
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if data == nil {
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t.Fatal("calculateIntradaySeries should not return nil for empty klines")
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}
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// All slices should be empty
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if len(data.MidPrices) != 0 {
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t.Errorf("MidPrices length = %d, want 0", len(data.MidPrices))
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}
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if len(data.Volume) != 0 {
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t.Errorf("Volume length = %d, want 0", len(data.Volume))
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}
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// ATR14 should be 0 (insufficient data)
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if data.ATR14 != 0 {
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t.Errorf("ATR14 = %.3f, want 0", data.ATR14)
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}
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}
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// TestCalculateIntradaySeries_VolumePrecision tests Volume precision preservation
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func TestCalculateIntradaySeries_VolumePrecision(t *testing.T) {
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klines := []Kline{
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{Close: 100.0, Volume: 1234.5678, High: 101.0, Low: 99.0},
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{Close: 101.0, Volume: 9876.5432, High: 102.0, Low: 100.0},
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{Close: 102.0, Volume: 5555.1111, High: 103.0, Low: 101.0},
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}
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data := calculateIntradaySeries(klines)
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expectedVolumes := []float64{1234.5678, 9876.5432, 5555.1111}
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for i, expected := range expectedVolumes {
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if data.Volume[i] != expected {
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t.Errorf("Volume[%d] = %.4f, want %.4f (precision not preserved)",
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i, data.Volume[i], expected)
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}
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}
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}
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// TestIsStaleData_NormalData tests that normal fluctuating data returns false
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func TestIsStaleData_NormalData(t *testing.T) {
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klines := []Kline{
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{Close: 100.0, Volume: 1000},
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{Close: 100.5, Volume: 1200},
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{Close: 99.8, Volume: 900},
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{Close: 100.2, Volume: 1100},
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{Close: 100.1, Volume: 950},
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}
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result := isStaleData(klines, "BTCUSDT")
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if result {
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t.Error("Expected false for normal fluctuating data, got true")
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}
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}
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// TestIsStaleData_PriceFreezeWithZeroVolume tests that frozen price + zero volume returns true
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func TestIsStaleData_PriceFreezeWithZeroVolume(t *testing.T) {
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klines := []Kline{
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{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
}
|
|
|
|
result := isStaleData(klines, "DOGEUSDT")
|
|
|
|
if !result {
|
|
t.Error("Expected true for frozen price + zero volume, got false")
|
|
}
|
|
}
|
|
|
|
// TestIsStaleData_PriceFreezeWithVolume tests that frozen price but normal volume returns false
|
|
func TestIsStaleData_PriceFreezeWithVolume(t *testing.T) {
|
|
klines := []Kline{
|
|
{Close: 100.0, Volume: 1000},
|
|
{Close: 100.0, Volume: 1200},
|
|
{Close: 100.0, Volume: 900},
|
|
{Close: 100.0, Volume: 1100},
|
|
{Close: 100.0, Volume: 950},
|
|
}
|
|
|
|
result := isStaleData(klines, "STABLECOIN")
|
|
|
|
if result {
|
|
t.Error("Expected false for frozen price but normal volume (low volatility market), got true")
|
|
}
|
|
}
|
|
|
|
// TestIsStaleData_InsufficientData tests that insufficient data (<5 klines) returns false
|
|
func TestIsStaleData_InsufficientData(t *testing.T) {
|
|
klines := []Kline{
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
}
|
|
|
|
result := isStaleData(klines, "BTCUSDT")
|
|
|
|
if result {
|
|
t.Error("Expected false for insufficient data (<5 klines), got true")
|
|
}
|
|
}
|
|
|
|
// TestIsStaleData_ExactlyFiveKlines tests edge case with exactly 5 klines
|
|
func TestIsStaleData_ExactlyFiveKlines(t *testing.T) {
|
|
// Stale case: exactly 5 frozen klines with zero volume
|
|
staleKlines := []Kline{
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
{Close: 100.0, Volume: 0},
|
|
}
|
|
|
|
result := isStaleData(staleKlines, "TESTUSDT")
|
|
if !result {
|
|
t.Error("Expected true for exactly 5 frozen klines with zero volume, got false")
|
|
}
|
|
|
|
// Normal case: exactly 5 klines with fluctuation
|
|
normalKlines := []Kline{
|
|
{Close: 100.0, Volume: 1000},
|
|
{Close: 100.1, Volume: 1100},
|
|
{Close: 99.9, Volume: 900},
|
|
{Close: 100.0, Volume: 1000},
|
|
{Close: 100.05, Volume: 950},
|
|
}
|
|
|
|
result = isStaleData(normalKlines, "TESTUSDT")
|
|
if result {
|
|
t.Error("Expected false for exactly 5 normal klines, got true")
|
|
}
|
|
}
|
|
|
|
// TestIsStaleData_WithinTolerance tests price changes within tolerance (0.01%)
|
|
func TestIsStaleData_WithinTolerance(t *testing.T) {
|
|
// Price changes within 0.01% tolerance should be treated as frozen
|
|
basePrice := 10000.0
|
|
tolerance := 0.0001 // 0.01%
|
|
smallChange := basePrice * tolerance * 0.5 // Half of tolerance
|
|
|
|
klines := []Kline{
|
|
{Close: basePrice, Volume: 1000},
|
|
{Close: basePrice + smallChange, Volume: 1000},
|
|
{Close: basePrice - smallChange, Volume: 1000},
|
|
{Close: basePrice, Volume: 1000},
|
|
{Close: basePrice + smallChange, Volume: 1000},
|
|
}
|
|
|
|
result := isStaleData(klines, "BTCUSDT")
|
|
|
|
// Should return false because there's normal volume despite tiny price changes
|
|
if result {
|
|
t.Error("Expected false for price within tolerance but with volume, got true")
|
|
}
|
|
}
|
|
|
|
// TestIsStaleData_MixedScenario tests realistic scenario with some history before freeze
|
|
func TestIsStaleData_MixedScenario(t *testing.T) {
|
|
// Simulate: normal trading → suddenly freezes
|
|
klines := []Kline{
|
|
{Close: 100.0, Volume: 1000}, // Normal
|
|
{Close: 100.5, Volume: 1200}, // Normal
|
|
{Close: 100.2, Volume: 1100}, // Normal
|
|
{Close: 50.0, Volume: 0}, // Freeze starts
|
|
{Close: 50.0, Volume: 0}, // Frozen
|
|
{Close: 50.0, Volume: 0}, // Frozen
|
|
{Close: 50.0, Volume: 0}, // Frozen
|
|
{Close: 50.0, Volume: 0}, // Frozen (last 5 are all frozen)
|
|
}
|
|
|
|
result := isStaleData(klines, "DOGEUSDT")
|
|
|
|
// Should detect stale data based on last 5 klines
|
|
if !result {
|
|
t.Error("Expected true for frozen last 5 klines with zero volume, got false")
|
|
}
|
|
}
|
|
|
|
// TestIsStaleData_EmptyKlines tests edge case with empty slice
|
|
func TestIsStaleData_EmptyKlines(t *testing.T) {
|
|
klines := []Kline{}
|
|
|
|
result := isStaleData(klines, "BTCUSDT")
|
|
|
|
if result {
|
|
t.Error("Expected false for empty klines, got true")
|
|
}
|
|
}
|
|
|
|
func TestCalculateDonchian(t *testing.T) {
|
|
// Create test klines with known high/low values
|
|
klines := []Kline{
|
|
{High: 100, Low: 90},
|
|
{High: 105, Low: 88},
|
|
{High: 102, Low: 92},
|
|
{High: 108, Low: 85},
|
|
{High: 103, Low: 91},
|
|
}
|
|
|
|
upper, lower := ExportCalculateDonchian(klines, 5)
|
|
|
|
if upper != 108 {
|
|
t.Errorf("Expected upper = 108, got %v", upper)
|
|
}
|
|
if lower != 85 {
|
|
t.Errorf("Expected lower = 85, got %v", lower)
|
|
}
|
|
}
|
|
|
|
func TestCalculateDonchian_PartialPeriod(t *testing.T) {
|
|
klines := []Kline{
|
|
{High: 100, Low: 90},
|
|
{High: 105, Low: 88},
|
|
}
|
|
|
|
upper, lower := ExportCalculateDonchian(klines, 10)
|
|
|
|
// Should use all available klines when period > len(klines)
|
|
if upper != 105 {
|
|
t.Errorf("Expected upper = 105, got %v", upper)
|
|
}
|
|
if lower != 88 {
|
|
t.Errorf("Expected lower = 88, got %v", lower)
|
|
}
|
|
}
|
|
|
|
func TestCalculateDonchian_InvalidPeriod(t *testing.T) {
|
|
klines := []Kline{
|
|
{High: 100, Low: 90},
|
|
}
|
|
|
|
// Zero period should return (0, 0)
|
|
upper, lower := ExportCalculateDonchian(klines, 0)
|
|
if upper != 0 || lower != 0 {
|
|
t.Errorf("Expected (0, 0) for zero period, got (%v, %v)", upper, lower)
|
|
}
|
|
|
|
// Negative period should return (0, 0)
|
|
upper, lower = ExportCalculateDonchian(klines, -1)
|
|
if upper != 0 || lower != 0 {
|
|
t.Errorf("Expected (0, 0) for negative period, got (%v, %v)", upper, lower)
|
|
}
|
|
}
|
|
|
|
func TestCalculateBoxData(t *testing.T) {
|
|
// Create synthetic kline data
|
|
klines := make([]Kline, 500)
|
|
for i := 0; i < 500; i++ {
|
|
basePrice := 100.0
|
|
klines[i] = Kline{
|
|
High: basePrice + float64(i%10),
|
|
Low: basePrice - float64(i%10),
|
|
Close: basePrice,
|
|
}
|
|
}
|
|
|
|
box := ExportCalculateBoxData(klines, 100.0)
|
|
|
|
if box.ShortUpper == 0 || box.ShortLower == 0 {
|
|
t.Error("Short box should not be zero")
|
|
}
|
|
if box.MidUpper == 0 || box.MidLower == 0 {
|
|
t.Error("Mid box should not be zero")
|
|
}
|
|
if box.LongUpper == 0 || box.LongLower == 0 {
|
|
t.Error("Long box should not be zero")
|
|
}
|
|
if box.CurrentPrice != 100.0 {
|
|
t.Errorf("Expected CurrentPrice = 100.0, got %v", box.CurrentPrice)
|
|
}
|
|
}
|