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Module moving_average

finance_solution::ta

Module moving_average 

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§Simple & exponential moving averages (SMA / EMA)

Teaching + production building blocks for price smoothers. Batch APIs (sma, ema) and incremental APIs (SmaState, EmaState) share one implementation path: batch is “create state → push_bars”.

§Word problem

A stock closed at 10, 11, 12, 13, 14 over five days. What is the 3-day SMA on day 5?

Expect: (12 + 13 + 14) / 3 = 13.

use finance_solution::stocks::ta::sma;
let closes = [10.0, 11.0, 12.0, 13.0, 14.0];
let s = sma(&closes, 3).unwrap();
// period index:     0     1     2     3     4
// warm-up:        None  None  Some  Some  Some
assert_eq!(s[0], None);
assert_eq!(s[1], None);
assert!((s[2].unwrap() - 11.0).abs() < 1e-12); // (10+11+12)/3
assert!((s[4].unwrap() - 13.0).abs() < 1e-12); // (12+13+14)/3

§Quant pattern — one pack, many symbols

use finance_solution::stocks::ta::{SmaState, EmaState};

// Live: hold state per symbol (your engine's HashMap)
let mut sma20 = SmaState::new(20).unwrap();
let mut ema20 = EmaState::new(20).unwrap();
// One streaming payload with several bars:
let _ = sma20.push_bars(&payload).unwrap();
let _ = ema20.push_bars(&payload).unwrap();
// Or single bar:
let last_sma = sma20.push(101.2).unwrap(); // Option after warm-up

§Formulas

SMA over window of length n:

SMA_t = (P_{t-n+1} + … + P_t) / n

EMA with span n (α = 2/(n+1)), seed = SMA of first n closes:

EMA_seed = SMA(P_0..P_{n-1})
EMA_t    = α * P_t + (1-α) * EMA_{t-1}

§Warm-up

Output length = input length. Indices 0 .. n-2 are None until the window is full.

Structs§

EmaState
Incremental EMA (α = 2/(period+1), seed = SMA of first period closes).
SmaState
Incremental SMA. After warm-up, each SmaState::push is O(1).

Functions§

ema
EMA with span period (α = 2 / (period + 1)). Seed = SMA of the first period closes.
ema_last
Last defined EMA value, if any (via EmaState).
sma
SMA of period closes. Leading period - 1 values are None.
sma_last
Last defined SMA value, if any.