execution_benchmarks.shape

// Execution Mode Benchmark Strategies
//
// These strategies are used to benchmark Interpreter vs VM vs JIT performance.
// Run with: cargo run --release --features jit -p shape-core --bin execution_mode_bench
//
// Performance (10k ES candles from DuckDB):
// | Strategy          | Interpreter | VM      | JIT          |
// |-------------------|-------------|---------|--------------|
// | simple_momentum   | 7,636/sec   | 7,797/s | 69.1M/sec    |
// | sma_crossover     | 610/sec     | 623/s   | 9.2M/sec     |
//
// JIT achieves ~9,000x speedup through:
// - Direct memory access (no interpreter overhead)
// - ~6 native instructions per candle field access
// - NaN-boxed values for fast type checks

// =============================================================================
// Strategy 1: Simple Momentum (JIT optimal - minimal operations)
// =============================================================================
// 2 candle reads, 1 division, 2 comparisons
// JIT: ~15ns per candle (69M candles/sec)

function simple_momentum() {
    let close = candle[0].close
    let prev_close = candle[-1].close
    let change = (close - prev_close) / prev_close

    if (change > 0.001) {
        return 1
    }
    if (change < -0.001) {
        return -1
    }
    return 0
}

// =============================================================================
// Strategy 2: SMA Crossover (Complex - many candle reads)
// =============================================================================
// 20+ candle reads, 10+ divisions, multiple comparisons
// JIT: ~108ns per candle (9.2M candles/sec)

function sma_crossover() {
    let close = candle[0].close
    let volume = candle[0].volume

    // Calculate 5-period SMA
    let sum5 = candle[0].close + candle[-1].close + candle[-2].close +
               candle[-3].close + candle[-4].close
    let sma5 = sum5 / 5

    // Calculate 10-period SMA (reuse sum5)
    let sum10 = sum5 + candle[-5].close + candle[-6].close +
                candle[-7].close + candle[-8].close + candle[-9].close
    let sma10 = sum10 / 10

    // Calculate average volume (5 periods)
    let vol_sum = candle[0].volume + candle[-1].volume + candle[-2].volume +
                  candle[-3].volume + candle[-4].volume
    let avg_vol = vol_sum / 5

    // Volume filter
    if (volume < avg_vol * 0.8) {
        return 0
    }

    // SMA crossover signals
    let prev_sma5 = (candle[-1].close + candle[-2].close + candle[-3].close +
                     candle[-4].close + candle[-5].close) / 5
    let prev_sma10 = (candle[-1].close + candle[-2].close + candle[-3].close +
                      candle[-4].close + candle[-5].close + candle[-6].close +
                      candle[-7].close + candle[-8].close + candle[-9].close +
                      candle[-10].close) / 10

    // Bullish crossover
    if (prev_sma5 <= prev_sma10 && sma5 > sma10) {
        return 1
    }

    // Bearish crossover
    if (prev_sma5 >= prev_sma10 && sma5 < sma10) {
        return -1
    }

    return 0
}

// =============================================================================
// Strategy 3: Momentum with Threshold Parameter (for sweep testing)
// =============================================================================
// Used to test parameter sweeps with BytecodeVM named argument support

function momentum_threshold(threshold) {
    let close = candle[0].close
    let prev = candle[-1].close
    let pct = (close - prev) / prev

    if (!in_position) {
        if (pct > threshold) {
            return "buy"
        }
    } else {
        if (pct < 0) {
            return "sell"
        }
    }
    return null
}

// =============================================================================
// Example: Run strategies
// =============================================================================

// Note: These are designed to be run via the execution_mode_bench binary
// for accurate timing, not via the CLI which has additional overhead.
//
// To run the benchmark:
// cargo run --release --features jit -p shape-core --bin execution_mode_bench -- --candles 10000
//
// The benchmark runs each strategy through Interpreter, VM, and JIT modes
// and reports candles/sec for each.