shape-runtime 0.3.2

Bytecode compiler, builtins, and runtime infrastructure for Shape
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91

/// @module std::finance::indicators::oscillators
/// Oscillators - Optimized with Intrinsics

from std::core::utils::rolling use { rolling_mean }
from std::finance::indicators::moving_averages use { ema }

/// Compute the relative strength index.
///
/// @see std::finance::indicators::moving_averages::ema
pub @warmup(period + 1) fn rsi(series, period = 14) {
    // Calculate price changes using diff intrinsic (FAST!)
    let changes = __intrinsic_diff(series);

    // Separate gains and losses
    let gains = changes.map(|x| max(x, 0));
    let losses = changes.map(|x| max(-x, 0));

    // Calculate average gains and losses using EMA (Wilder's Smoothing is essentially EMA)
    // Note: Wilder's smoothing is EMA with alpha = 1/period, whereas standard EMA is 2/(period+1).
    // Standard RSI uses Wilder's.
    // We should use a specific 'wilders' or 'rma' function if we want exact RSI.
    // For now, using standard EMA as placeholder or if compatible.
    // To match RSI exactly: alpha = 1/period.
    // Standard EMA: alpha = 2/(period+1).
    // So 'ema' function with period = 2*period - 1 would match?
    // 2 / ( (2n-1) + 1 ) = 2/2n = 1/n.
    // So ema(..., 2*period - 1) approximates Wilder's.
    
    let wilder_period = 2 * period - 1;
    let avg_gain = ema(gains, wilder_period);
    let avg_loss = ema(losses, wilder_period);

    // Calculate RS and RSI
    let rs = avg_gain / avg_loss;
    100 - (100 / (1 + rs))
}

/// Compute the stochastic oscillator `%K` and `%D` series.
pub @warmup(k_period + d_period) fn stochastic(high, low, close, k_period = 14, d_period = 3) {
    // Find highest high and lowest low using intrinsics (FAST!)
    let highest_high = __intrinsic_rolling_max(high, k_period);
    let lowest_low = __intrinsic_rolling_min(low, k_period);

    // Calculate %K
    let k = ((close - lowest_low) / (highest_high - lowest_low)) * 100;

    // Calculate %D (SMA of %K) using vector rolling_mean
    let d = rolling_mean(k, d_period);

    {
        k: k,
        d: d
    }
}

/// Compute the commodity channel index.
pub @warmup(period) fn cci(high, low, close, period = 20) {
    // Typical price
    let tp = (high + low + close) / 3;

    // SMA of typical price
    let sma_tp = rolling_mean(tp, period);

    // Mean deviation
    let deviations = abs(tp - sma_tp);
    let mean_dev = rolling_mean(deviations, period);

    // CCI formula
    (tp - sma_tp) / (0.015 * mean_dev)
}

/// Compute the rate of change in percent.
pub @warmup(period + 1) fn roc(series, period) {
    // Use pct_change intrinsic with custom period
    let pct = __intrinsic_pct_change(series, period);
    pct * 100  // Convert to percentage
}

/// Compute the raw momentum over `period`.
pub @warmup(period + 1) fn momentum(series, period) {
    // Simple difference over period
    __intrinsic_diff(series, period)
}

/// Compute Williams `%R`.
pub @warmup(period) fn williams_r(high, low, close, period = 14) {
    let highest_high = __intrinsic_rolling_max(high, period);
    let lowest_low = __intrinsic_rolling_min(low, period);

    ((highest_high - close) / (highest_high - lowest_low)) * -100
}