Best Practices

1// ❌ Bad: Recalculates entire SMA from scratch for every bar2function calculate(bars: BarData[], params: any) {3  return bars.map((bar, i) => {4    // O(n) per bar → O(n²) total5    const slice = bars.slice(Math.max(0, i - params.period + 1), i + 1);6    const sum = slice.reduce((s, b) => s + b.close, 0);7    return { time: bar.time, value: sum / slice.length };8  });9}1011// ✅ Good: Running sum — O(1) per bar → O(n) total12function calculate(bars: BarData[], params: any) {13  const period = params.period;14  const result: IndicatorValue[] = [];15  let sum = 0;1617  for (let i = 0; i < bars.length; i++) {18    sum += bars[i].close;19    if (i >= period) sum -= bars[i - period].close;20    if (i >= period - 1) {21      result.push({ time: bars[i].time, value: sum / period });22    }23  }24  return result;25}

1// ❌ Bad: Multiple save/restore per element2function render(ctx, values) {3  values.forEach(v => {4    ctx.save();              // Expensive per iteration5    ctx.strokeStyle = v.color;6    ctx.beginPath();7    ctx.moveTo(v.x, v.y);8    ctx.lineTo(v.x2, v.y2);9    ctx.stroke();10    ctx.restore();           // Expensive per iteration11  });12}1314// ✅ Good: Batch by color, single save/restore15function render(ctx, values) {16  ctx.save();17  18  // Group by color19  const groups = new Map<string, typeof values>();20  values.forEach(v => {21    if (!groups.has(v.color)) groups.set(v.color, []);22    groups.get(v.color)!.push(v);23  });24  25  // Draw each color group in one path26  groups.forEach((items, color) => {27    ctx.strokeStyle = color;28    ctx.beginPath();29    items.forEach(v => {30      ctx.moveTo(v.x, v.y);31      ctx.lineTo(v.x2, v.y2);32    });33    ctx.stroke();  // Single stroke call per color34  });35  36  ctx.restore();37}

1# ❌ Bad: Row-by-row iteration in Python2for i in range(len(df)):3    if df.iloc[i]['close'] > df.iloc[i]['sma_20']:4        signals.append('BUY')56# ✅ Good: Vectorized with pandas/numpy7signals = np.where(df['close'] > df['sma_20'], 'BUY', 'HOLD')89# ❌ Bad: Recomputing entire rolling window10df['sma'] = df['close'].apply(lambda x: df['close'].rolling(20).mean())1112# ✅ Good: Single vectorized operation13df['sma'] = df['close'].rolling(20).mean()

1// Always handle insufficient data2function calculate(bars: BarData[], params: any) {3  const period = params.period;4  if (bars.length < period) return [];  // Not enough data56  // Handle NaN values7  const output = computeIndicator(bars, period);8  return output.filter(v => !isNaN(v.value) && isFinite(v.value));9}1011// Handle division by zero12function rsi(gains: number, losses: number): number {13  if (losses === 0) return 100;   // No losses = max RSI14  if (gains === 0) return 0;     // No gains = min RSI15  return 100 - (100 / (1 + gains / losses));16}

1// Validate params before use2function calculate(bars: BarData[], params: any) {3  const period = Math.max(1, Math.floor(params.period || 14));4  const multiplier = Math.max(0.1, params.multiplier || 2.0);5  const source = params.source || 'close';6  7  // Validate source8  const validSources = ['close', 'open', 'high', 'low', 'hl2', 'hlc3', 'ohlc4'];9  if (!validSources.includes(source)) {10    throw new Error(`Invalid source: ${source}. Use one of: ${validSources.join(', ')}`);11  }12}

1def generate_signal(bars, position, config):2    try:3        df = pd.DataFrame(bars)4        if len(df) < config.get('min_bars', 50):5            return {'signal': Signal.HOLD, 'reason': 'Insufficient data'}6        7        # Your strategy logic8        signal = compute_signal(df, config)9        return signal10        11    except Exception as e:12        # Never let uncaught exceptions crash the strategy13        return {14            'signal': Signal.HOLD,15            'reason': f'Error: {str(e)}',16            'error': True,17        }

1// ✅ Good structure for an indicator script:23// 1. Metadata (required)4export const metadata = { ... };56// 2. Default configuration (required)7export const defaultConfig = { ... };89// 3. Helper functions (internal, not exported)10function computeSMA(values: number[], period: number): number[] { ... }11function computeEMA(values: number[], period: number): number[] { ... }1213// 4. Main calculation (required export)14export function calculate(bars: BarData[], params: any) { ... }1516// 5. Custom render (optional export)17export function render(ctx: CanvasRenderingContext2D, ...) { ... }