Files
nofx/market/data_test.go
T
tinkle-community 7e96c5d0f2 Ai grid (#1344)
* 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
2026-01-19 12:07:14 +08:00

586 lines
16 KiB
Go

package market
import (
"math"
"testing"
)
// generateTestKlines generates test K-line data
func generateTestKlines(count int) []Kline {
klines := make([]Kline, count)
for i := 0; i < count; i++ {
// Generate simulated price data with some fluctuation
basePrice := 100.0
variance := float64(i%10) * 0.5
open := basePrice + variance
high := open + 1.0
low := open - 0.5
close := open + 0.3
volume := 1000.0 + float64(i*100)
klines[i] = Kline{
OpenTime: int64(i * 180000), // 3-minute interval
Open: open,
High: high,
Low: low,
Close: close,
Volume: volume,
CloseTime: int64((i+1)*180000 - 1),
}
}
return klines
}
// TestCalculateIntradaySeries_VolumeCollection tests Volume data collection
func TestCalculateIntradaySeries_VolumeCollection(t *testing.T) {
tests := []struct {
name string
klineCount int
expectedVolLen int
}{
{
name: "Normal case - 20 K-lines",
klineCount: 20,
expectedVolLen: 10, // Should collect latest 10
},
{
name: "Exactly 10 K-lines",
klineCount: 10,
expectedVolLen: 10,
},
{
name: "Less than 10 K-lines",
klineCount: 5,
expectedVolLen: 5, // Should return all 5
},
{
name: "More than 10 K-lines",
klineCount: 30,
expectedVolLen: 10, // Should only return latest 10
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
klines := generateTestKlines(tt.klineCount)
data := calculateIntradaySeries(klines)
if data == nil {
t.Fatal("calculateIntradaySeries returned nil")
}
if len(data.Volume) != tt.expectedVolLen {
t.Errorf("Volume length = %d, want %d", len(data.Volume), tt.expectedVolLen)
}
// Verify Volume data correctness
if len(data.Volume) > 0 {
// Calculate expected start index
start := tt.klineCount - 10
if start < 0 {
start = 0
}
// Verify first Volume value
expectedFirstVolume := klines[start].Volume
if data.Volume[0] != expectedFirstVolume {
t.Errorf("First volume = %.2f, want %.2f", data.Volume[0], expectedFirstVolume)
}
// Verify last Volume value
expectedLastVolume := klines[tt.klineCount-1].Volume
lastVolume := data.Volume[len(data.Volume)-1]
if lastVolume != expectedLastVolume {
t.Errorf("Last volume = %.2f, want %.2f", lastVolume, expectedLastVolume)
}
}
})
}
}
// TestCalculateIntradaySeries_VolumeValues tests Volume value correctness
func TestCalculateIntradaySeries_VolumeValues(t *testing.T) {
klines := []Kline{
{Close: 100.0, Volume: 1000.0, High: 101.0, Low: 99.0, Open: 100.0},
{Close: 101.0, Volume: 1100.0, High: 102.0, Low: 100.0, Open: 101.0},
{Close: 102.0, Volume: 1200.0, High: 103.0, Low: 101.0, Open: 102.0},
{Close: 103.0, Volume: 1300.0, High: 104.0, Low: 102.0, Open: 103.0},
{Close: 104.0, Volume: 1400.0, High: 105.0, Low: 103.0, Open: 104.0},
{Close: 105.0, Volume: 1500.0, High: 106.0, Low: 104.0, Open: 105.0},
{Close: 106.0, Volume: 1600.0, High: 107.0, Low: 105.0, Open: 106.0},
{Close: 107.0, Volume: 1700.0, High: 108.0, Low: 106.0, Open: 107.0},
{Close: 108.0, Volume: 1800.0, High: 109.0, Low: 107.0, Open: 108.0},
{Close: 109.0, Volume: 1900.0, High: 110.0, Low: 108.0, Open: 109.0},
}
data := calculateIntradaySeries(klines)
expectedVolumes := []float64{1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0, 1900.0}
if len(data.Volume) != len(expectedVolumes) {
t.Fatalf("Volume length = %d, want %d", len(data.Volume), len(expectedVolumes))
}
for i, expected := range expectedVolumes {
if data.Volume[i] != expected {
t.Errorf("Volume[%d] = %.2f, want %.2f", i, data.Volume[i], expected)
}
}
}
// TestCalculateIntradaySeries_ATR14 tests ATR14 calculation
func TestCalculateIntradaySeries_ATR14(t *testing.T) {
tests := []struct {
name string
klineCount int
expectZero bool
expectNonZero bool
}{
{
name: "Sufficient data - 20 K-lines",
klineCount: 20,
expectNonZero: true,
},
{
name: "Exactly 15 K-lines (ATR14 requires at least 15)",
klineCount: 15,
expectNonZero: true,
},
{
name: "Insufficient data - 14 K-lines",
klineCount: 14,
expectZero: true,
},
{
name: "Insufficient data - 10 K-lines",
klineCount: 10,
expectZero: true,
},
{
name: "Insufficient data - 5 K-lines",
klineCount: 5,
expectZero: true,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
klines := generateTestKlines(tt.klineCount)
data := calculateIntradaySeries(klines)
if data == nil {
t.Fatal("calculateIntradaySeries returned nil")
}
if tt.expectZero && data.ATR14 != 0 {
t.Errorf("ATR14 = %.3f, expected 0 (insufficient data)", data.ATR14)
}
if tt.expectNonZero && data.ATR14 <= 0 {
t.Errorf("ATR14 = %.3f, expected > 0", data.ATR14)
}
})
}
}
// TestCalculateATR tests ATR calculation function
func TestCalculateATR(t *testing.T) {
tests := []struct {
name string
klines []Kline
period int
expectZero bool
}{
{
name: "Normal calculation - sufficient data",
klines: []Kline{
{High: 102.0, Low: 100.0, Close: 101.0},
{High: 103.0, Low: 101.0, Close: 102.0},
{High: 104.0, Low: 102.0, Close: 103.0},
{High: 105.0, Low: 103.0, Close: 104.0},
{High: 106.0, Low: 104.0, Close: 105.0},
{High: 107.0, Low: 105.0, Close: 106.0},
{High: 108.0, Low: 106.0, Close: 107.0},
{High: 109.0, Low: 107.0, Close: 108.0},
{High: 110.0, Low: 108.0, Close: 109.0},
{High: 111.0, Low: 109.0, Close: 110.0},
{High: 112.0, Low: 110.0, Close: 111.0},
{High: 113.0, Low: 111.0, Close: 112.0},
{High: 114.0, Low: 112.0, Close: 113.0},
{High: 115.0, Low: 113.0, Close: 114.0},
{High: 116.0, Low: 114.0, Close: 115.0},
},
period: 14,
expectZero: false,
},
{
name: "Insufficient data - equal to period",
klines: []Kline{
{High: 102.0, Low: 100.0, Close: 101.0},
{High: 103.0, Low: 101.0, Close: 102.0},
},
period: 2,
expectZero: true,
},
{
name: "Insufficient data - less than period",
klines: []Kline{
{High: 102.0, Low: 100.0, Close: 101.0},
},
period: 14,
expectZero: true,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
atr := calculateATR(tt.klines, tt.period)
if tt.expectZero {
if atr != 0 {
t.Errorf("calculateATR() = %.3f, expected 0 (insufficient data)", atr)
}
} else {
if atr <= 0 {
t.Errorf("calculateATR() = %.3f, expected > 0", atr)
}
}
})
}
}
// TestCalculateATR_TrueRange tests ATR True Range calculation correctness
func TestCalculateATR_TrueRange(t *testing.T) {
// Create a simple test case, manually calculate expected ATR
klines := []Kline{
{High: 50.0, Low: 48.0, Close: 49.0}, // TR = 2.0
{High: 51.0, Low: 49.0, Close: 50.0}, // TR = max(2.0, 2.0, 1.0) = 2.0
{High: 52.0, Low: 50.0, Close: 51.0}, // TR = max(2.0, 2.0, 1.0) = 2.0
{High: 53.0, Low: 51.0, Close: 52.0}, // TR = 2.0
{High: 54.0, Low: 52.0, Close: 53.0}, // TR = 2.0
}
atr := calculateATR(klines, 3)
// Expected calculation:
// TR[1] = max(51-49, |51-49|, |49-49|) = 2.0
// TR[2] = max(52-50, |52-50|, |50-50|) = 2.0
// TR[3] = max(53-51, |53-51|, |51-51|) = 2.0
// Initial ATR = (2.0 + 2.0 + 2.0) / 3 = 2.0
// TR[4] = max(54-52, |54-52|, |52-52|) = 2.0
// Smoothed ATR = (2.0*2 + 2.0) / 3 = 2.0
expectedATR := 2.0
tolerance := 0.01 // Allow small floating point error
if math.Abs(atr-expectedATR) > tolerance {
t.Errorf("calculateATR() = %.3f, want approximately %.3f", atr, expectedATR)
}
}
// TestCalculateIntradaySeries_ConsistencyWithOtherIndicators tests Volume and other indicators consistency
func TestCalculateIntradaySeries_ConsistencyWithOtherIndicators(t *testing.T) {
klines := generateTestKlines(30)
data := calculateIntradaySeries(klines)
// All arrays should exist
if data.MidPrices == nil {
t.Error("MidPrices should not be nil")
}
if data.Volume == nil {
t.Error("Volume should not be nil")
}
// MidPrices and Volume should have the same length (both latest 10)
if len(data.MidPrices) != len(data.Volume) {
t.Errorf("MidPrices length (%d) should equal Volume length (%d)",
len(data.MidPrices), len(data.Volume))
}
// All Volume values should be > 0
for i, vol := range data.Volume {
if vol <= 0 {
t.Errorf("Volume[%d] = %.2f, should be > 0", i, vol)
}
}
}
// TestCalculateIntradaySeries_EmptyKlines tests empty K-line data
func TestCalculateIntradaySeries_EmptyKlines(t *testing.T) {
klines := []Kline{}
data := calculateIntradaySeries(klines)
if data == nil {
t.Fatal("calculateIntradaySeries should not return nil for empty klines")
}
// All slices should be empty
if len(data.MidPrices) != 0 {
t.Errorf("MidPrices length = %d, want 0", len(data.MidPrices))
}
if len(data.Volume) != 0 {
t.Errorf("Volume length = %d, want 0", len(data.Volume))
}
// ATR14 should be 0 (insufficient data)
if data.ATR14 != 0 {
t.Errorf("ATR14 = %.3f, want 0", data.ATR14)
}
}
// TestCalculateIntradaySeries_VolumePrecision tests Volume precision preservation
func TestCalculateIntradaySeries_VolumePrecision(t *testing.T) {
klines := []Kline{
{Close: 100.0, Volume: 1234.5678, High: 101.0, Low: 99.0},
{Close: 101.0, Volume: 9876.5432, High: 102.0, Low: 100.0},
{Close: 102.0, Volume: 5555.1111, High: 103.0, Low: 101.0},
}
data := calculateIntradaySeries(klines)
expectedVolumes := []float64{1234.5678, 9876.5432, 5555.1111}
for i, expected := range expectedVolumes {
if data.Volume[i] != expected {
t.Errorf("Volume[%d] = %.4f, want %.4f (precision not preserved)",
i, data.Volume[i], expected)
}
}
}
// TestIsStaleData_NormalData tests that normal fluctuating data returns false
func TestIsStaleData_NormalData(t *testing.T) {
klines := []Kline{
{Close: 100.0, Volume: 1000},
{Close: 100.5, Volume: 1200},
{Close: 99.8, Volume: 900},
{Close: 100.2, Volume: 1100},
{Close: 100.1, Volume: 950},
}
result := isStaleData(klines, "BTCUSDT")
if result {
t.Error("Expected false for normal fluctuating data, got true")
}
}
// TestIsStaleData_PriceFreezeWithZeroVolume tests that frozen price + zero volume returns true
func TestIsStaleData_PriceFreezeWithZeroVolume(t *testing.T) {
klines := []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(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)
}
}