Module 10 · Financial Time-Series Foundations Lesson 94 of 120
Rolling and Expanding Windows
Choosing how much history a monitoring statistic remembers.
Transcript
20 sentences · select one to jump thereCheck your understanding
Why is the final rolling mean six while the expanding mean is five?
Code lab
Run it yourself
The lesson source in 7 languages. Edit it, run TypeScript and Python right here, and compare with the expected output.
/**
* Fintech Math Bootcamp · Lesson 094 of 120
* Rolling and Expanding Windows
* Module 10: Financial Time-Series Foundations
*
* Scenario: Choosing how much history a monitoring statistic remembers
* Rule: rolling: last w observations; expanding: all observations so far
*
* Try it: Why is the final rolling mean six while the expanding mean is five?
*
* Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
* Free course: https://courses.thefintechbuilder.com
* Synthetic teaching example, not financial advice or a production library.
*/
export function lesson094() {
const x=[2,4,6,8],w=3;
const avg=(a:number[])=>a.reduce((s,v)=>s+v,0)/a.length;
const rolling=x.map((_,i)=>i+1<w?null:avg(x.slice(i-w+1,i+1)));
const expanding=x.map((_,i)=>avg(x.slice(0,i+1)));
const result={rolling,expanding};
return result;
}
export const checkedResult = {"rolling":[null,null,4,6],"expanding":[2,3,4,5]};
// Run this file directly: npx tsx lessons/10-financial-time-series-foundations/094-rolling-and-expanding-windows.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
console.log(JSON.stringify(lesson094(), null, 2));
}
Your output
Press Run to execute the code in your browser.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}# Fintech Math Bootcamp · Lesson 094 of 120
# Rolling and Expanding Windows
# Module 10: Financial Time-Series Foundations
#
# Scenario: Choosing how much history a monitoring statistic remembers
# Rule: rolling: last w observations; expanding: all observations so far
#
# Try it: Why is the final rolling mean six while the expanding mean is five?
#
# Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
# Free course: https://courses.thefintechbuilder.com
# Synthetic teaching example, not financial advice or a production library.
#
# Run: python main.py
import json
def lesson_094():
x, w = [2, 4, 6, 8], 3
def avg(a):
return sum(a) / len(a)
rolling = [None if i + 1 < w else avg(x[i - w + 1:i + 1]) for i in range(len(x))]
expanding = [avg(x[:i + 1]) for i in range(len(x))]
return {"rolling": rolling, "expanding": expanding}
if __name__ == "__main__":
print(json.dumps(lesson_094(), indent=2))
Your output
Press Run to execute the code in your browser.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}/*
* Fintech Math Bootcamp - Lesson 094 of 120
* Rolling and Expanding Windows
* Module 10: Financial Time-Series Foundations
*
* Scenario: Choosing how much history a monitoring statistic remembers
* Rule: rolling: last w observations; expanding: all observations so far
*
* Try it: Why is the final rolling mean six while the expanding mean is five?
*
* Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
* Free course: https://courses.thefintechbuilder.com
* Synthetic teaching example, not financial advice or a production library.
*
* Run: javac Main.java && java Main
*/
import java.util.*;
public class Main {
static double avg(double[] a, int from, int to) {
double s = 0;
for (int i = from; i < to; i++) s += a[i];
return s / (to - from);
}
static Map<String, Object> lesson094() {
double[] x = {2, 4, 6, 8};
int w = 3;
List<Double> rolling = new ArrayList<Double>();
List<Double> expanding = new ArrayList<Double>();
for (int i = 0; i < x.length; i++) {
rolling.add(i + 1 < w ? null : Double.valueOf(avg(x, i - w + 1, i + 1)));
expanding.add(avg(x, 0, i + 1));
}
return obj("rolling", rolling, "expanding", expanding);
}
public static void main(String[] args) {
System.out.println(toJson(lesson094(), ""));
}
// Minimal JSON writer: insertion-ordered Map, List, Number, Boolean, String and null.
static String toJson(Object value, String indent) {
if (value == null) return "null";
if (value instanceof String) return quote((String) value);
if (value instanceof Boolean) return value.toString();
if (value instanceof Double) return formatNumber((Double) value);
if (value instanceof Number) return value.toString();
String inner = indent + " ";
StringBuilder sb = new StringBuilder();
if (value instanceof Map) {
Map<?, ?> map = (Map<?, ?>) value;
if (map.isEmpty()) return "{}";
sb.append("{\n");
int i = 0;
for (Map.Entry<?, ?> e : map.entrySet()) {
sb.append(inner).append(quote(e.getKey().toString())).append(": ").append(toJson(e.getValue(), inner));
sb.append(++i < map.size() ? ",\n" : "\n");
}
return sb.append(indent).append("}").toString();
}
List<?> list = (List<?>) value;
if (list.isEmpty()) return "[]";
sb.append("[\n");
for (int i = 0; i < list.size(); i++) {
sb.append(inner).append(toJson(list.get(i), inner)).append(i + 1 < list.size() ? ",\n" : "\n");
}
return sb.append(indent).append("]").toString();
}
// Integral doubles print without ".0", as JavaScript does; others use Java's round-trip form.
static String formatNumber(double x) {
if (x == Math.rint(x) && Math.abs(x) < 1e15) return Long.toString((long) x);
return Double.toString(x);
}
static String quote(String s) {
StringBuilder sb = new StringBuilder("\"");
for (char c : s.toCharArray()) {
if (c == '"' || c == '\\') sb.append('\\').append(c);
else if (c < 0x20) sb.append(String.format("\\u%04x", (int) c));
else sb.append(c);
}
return sb.append('"').toString();
}
// Builds an insertion-ordered object from alternating keys and values.
static Map<String, Object> obj(Object... keysAndValues) {
Map<String, Object> map = new LinkedHashMap<String, Object>();
for (int i = 0; i < keysAndValues.length; i += 2) map.put((String) keysAndValues[i], keysAndValues[i + 1]);
return map;
}
static List<Object> list(double... values) {
List<Object> out = new ArrayList<Object>();
for (double v : values) out.add(v);
return out;
}
}
No browser runner for Java yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}// Fintech Math Bootcamp · Lesson 094 of 120
// Rolling and Expanding Windows
// Module 10: Financial Time-Series Foundations
//
// Scenario: Choosing how much history a monitoring statistic remembers
// Rule: rolling: last w observations; expanding: all observations so far
//
// Try it: Why is the final rolling mean six while the expanding mean is five?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
//
// Run: go run main.go
package main
import (
"encoding/json"
"fmt"
)
type Result struct {
Rolling []any `json:"rolling"`
Expanding []float64 `json:"expanding"`
}
func avg(a []float64) float64 {
s := 0.0
for _, v := range a {
s += v
}
return s / float64(len(a))
}
func lesson094() Result {
x, w := []float64{2, 4, 6, 8}, 3
rolling := make([]any, len(x)) // nil until the window is full
expanding := make([]float64, len(x))
for i := range x {
if i+1 >= w {
rolling[i] = avg(x[i-w+1 : i+1])
}
expanding[i] = avg(x[:i+1])
}
return Result{Rolling: rolling, Expanding: expanding}
}
func main() {
out, err := json.MarshalIndent(lesson094(), "", " ")
if err != nil {
panic(err)
}
fmt.Println(string(out))
}
No browser runner for Go yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}// Fintech Math Bootcamp · Lesson 094 of 120
// Rolling and Expanding Windows
// Module 10: Financial Time-Series Foundations
//
// Scenario: Choosing how much history a monitoring statistic remembers
// Rule: rolling: last w observations; expanding: all observations so far
//
// Try it: Why is the final rolling mean six while the expanding mean is five?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
//
// Run: g++ -std=c++17 -O1 -o lesson main.cpp && ./lesson
#include <algorithm>
#include <charconv>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <deque>
#include <iostream>
#include <limits>
#include <map>
#include <optional>
#include <stdexcept>
#include <string>
#include <utility>
#include <variant>
#include <vector>
// Minimal JSON value: objects keep insertion order; numbers print in shortest round-trip form.
struct Json {
enum class Kind { Null, Bool, Number, String, Array, Object };
Kind kind = Kind::Null;
bool flag = false;
double number = 0.0;
std::string text;
std::vector<std::string> keys; // object keys, parallel to items
std::vector<Json> items; // array items, or object values
};
Json jnull() { return Json{}; }
Json jbool(bool value) { Json j; j.kind = Json::Kind::Bool; j.flag = value; return j; }
Json jnum(double value) { Json j; j.kind = Json::Kind::Number; j.number = value; return j; }
Json jstr(const std::string& value) { Json j; j.kind = Json::Kind::String; j.text = value; return j; }
Json jarr(std::vector<Json> items) { Json j; j.kind = Json::Kind::Array; j.items = std::move(items); return j; }
Json jobj(std::vector<std::pair<std::string, Json>> fields) {
Json j;
j.kind = Json::Kind::Object;
for (auto& field : fields) {
j.keys.push_back(field.first);
j.items.push_back(std::move(field.second));
}
return j;
}
Json jnums(const std::vector<double>& values) {
std::vector<Json> items;
for (double v : values) items.push_back(jnum(v));
return jarr(std::move(items));
}
Json jnums(const std::vector<std::optional<double>>& values) {
std::vector<Json> items;
for (const auto& v : values) items.push_back(v ? jnum(*v) : jnull());
return jarr(std::move(items));
}
std::string formatNumber(double value) {
char buffer[64];
auto result = std::to_chars(buffer, buffer + sizeof buffer, value); // shortest round-trip form
return std::string(buffer, result.ptr);
}
std::string quote(const std::string& text) {
std::string out = "\"";
for (char c : text) {
if (c == '"' || c == '\\') {
out += '\\';
out += c;
} else if (static_cast<unsigned char>(c) < 0x20) {
char buffer[8];
std::snprintf(buffer, sizeof buffer, "\\u%04x", static_cast<unsigned>(c));
out += buffer;
} else {
out += c;
}
}
return out + "\"";
}
void writeJson(std::ostream& out, const Json& value, const std::string& indent = "") {
const std::string inner = indent + " ";
switch (value.kind) {
case Json::Kind::Null: out << "null"; return;
case Json::Kind::Bool: out << (value.flag ? "true" : "false"); return;
case Json::Kind::Number: out << formatNumber(value.number); return;
case Json::Kind::String: out << quote(value.text); return;
case Json::Kind::Array:
case Json::Kind::Object: {
const bool isObject = value.kind == Json::Kind::Object;
if (value.items.empty()) {
out << (isObject ? "{}" : "[]");
return;
}
out << (isObject ? "{\n" : "[\n");
for (std::size_t i = 0; i < value.items.size(); ++i) {
out << inner;
if (isObject) out << quote(value.keys[i]) << ": ";
writeJson(out, value.items[i], inner);
out << (i + 1 < value.items.size() ? ",\n" : "\n");
}
out << indent << (isObject ? "}" : "]");
}
}
}
double avg(const std::vector<double>& a, std::size_t from, std::size_t to) {
double s = 0;
for (std::size_t i = from; i < to; ++i) s += a[i];
return s / static_cast<double>(to - from);
}
Json lesson094() {
const std::vector<double> x{2, 4, 6, 8};
const std::size_t w = 3;
std::vector<std::optional<double>> rolling;
std::vector<double> expanding;
for (std::size_t i = 0; i < x.size(); ++i) {
rolling.push_back(i + 1 < w ? std::nullopt : std::optional<double>(avg(x, i + 1 - w, i + 1)));
expanding.push_back(avg(x, 0, i + 1));
}
return jobj({{"rolling", jnums(rolling)}, {"expanding", jnums(expanding)}});
}
int main() {
writeJson(std::cout, lesson094());
std::cout << "\n";
return 0;
}
No browser runner for C++ yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}// Fintech Math Bootcamp · Lesson 094 of 120
// Rolling and Expanding Windows
// Module 10: Financial Time-Series Foundations
//
// Scenario: Choosing how much history a monitoring statistic remembers
// Rule: rolling: last w observations; expanding: all observations so far
//
// Try it: Why is the final rolling mean six while the expanding mean is five?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
//
// Run: rustc -O main.rs && ./main
#![allow(dead_code)]
/// Minimal JSON value; objects keep insertion order.
enum Json {
Null,
Bool(bool),
Num(f64),
Str(String),
Arr(Vec<Json>),
Obj(Vec<(String, Json)>),
}
fn obj(fields: Vec<(&str, Json)>) -> Json {
Json::Obj(fields.into_iter().map(|(k, v)| (k.to_string(), v)).collect())
}
fn nums(values: &[f64]) -> Json {
Json::Arr(values.iter().map(|&v| Json::Num(v)).collect())
}
fn optional_nums(values: &[Option<f64>]) -> Json {
Json::Arr(values.iter().map(|v| v.map_or(Json::Null, Json::Num)).collect())
}
fn quote(text: &str) -> String {
let mut out = String::from("\"");
for c in text.chars() {
match c {
'"' => out.push_str("\\\""),
'\\' => out.push_str("\\\\"),
c if (c as u32) < 0x20 => out.push_str(&format!("\\u{:04x}", c as u32)),
c => out.push(c),
}
}
out.push('"');
out
}
// Display for f64 prints the shortest string that round-trips, as JavaScript does.
fn write_json(value: &Json, indent: &str, out: &mut String) {
let inner = format!("{} ", indent);
match value {
Json::Null => out.push_str("null"),
Json::Bool(b) => out.push_str(if *b { "true" } else { "false" }),
Json::Num(n) => out.push_str(&format!("{}", n)),
Json::Str(s) => out.push_str("e(s)),
Json::Arr(items) if items.is_empty() => out.push_str("[]"),
Json::Obj(fields) if fields.is_empty() => out.push_str("{}"),
Json::Arr(items) => {
out.push_str("[\n");
for (i, item) in items.iter().enumerate() {
out.push_str(&inner);
write_json(item, &inner, out);
out.push_str(if i + 1 < items.len() { ",\n" } else { "\n" });
}
out.push_str(indent);
out.push(']');
}
Json::Obj(fields) => {
out.push_str("{\n");
for (i, (key, item)) in fields.iter().enumerate() {
out.push_str(&inner);
out.push_str("e(key));
out.push_str(": ");
write_json(item, &inner, out);
out.push_str(if i + 1 < fields.len() { ",\n" } else { "\n" });
}
out.push_str(indent);
out.push('}');
}
}
}
fn avg(a: &[f64]) -> f64 {
a.iter().fold(0.0, |s, v| s + v) / a.len() as f64
}
fn lesson_094() -> Json {
let (x, w): ([f64; 4], usize) = ([2.0, 4.0, 6.0, 8.0], 3);
let rolling: Vec<Option<f64>> = (0..x.len())
.map(|i| if i + 1 < w { None } else { Some(avg(&x[i + 1 - w..i + 1])) })
.collect();
let expanding: Vec<f64> = (0..x.len()).map(|i| avg(&x[..i + 1])).collect();
obj(vec![("rolling", optional_nums(&rolling)), ("expanding", nums(&expanding))])
}
fn main() {
let mut out = String::new();
write_json(&lesson_094(), "", &mut out);
println!("{}", out);
}
No browser runner for Rust yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}// Fintech Math Bootcamp · Lesson 094 of 120
// Rolling and Expanding Windows
// Module 10: Financial Time-Series Foundations
//
// Scenario: Choosing how much history a monitoring statistic remembers
// Rule: rolling: last w observations; expanding: all observations so far
//
// Try it: Why is the final rolling mean six while the expanding mean is five?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-time-series-foundations/rolling-and-expanding-windows/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
//
// Run: dotnet run
using System;
using System.Collections.Generic;
using System.Globalization;
using System.Linq;
using System.Text.Encodings.Web;
using System.Text.Json;
var options = new JsonSerializerOptions { WriteIndented = true, Encoder = JavaScriptEncoder.UnsafeRelaxedJsonEscaping };
Console.WriteLine(JsonSerializer.Serialize(Lesson094(), options));
static object Lesson094()
{
double[] x = { 2, 4, 6, 8 };
int w = 3;
double Avg(IEnumerable<double> a) => a.Aggregate(0.0, (s, v) => s + v) / a.Count();
double?[] rolling = x.Select((_, i) => i + 1 < w ? (double?)null : Avg(x.Skip(i - w + 1).Take(w))).ToArray();
double[] expanding = x.Select((_, i) => Avg(x.Take(i + 1))).ToArray();
return new { rolling, expanding };
}
No browser runner for C# yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"rolling": [
null,
null,
4,
6
],
"expanding": [
2,
3,
4,
5
]
}Prefer your own machine? Every file is in the course repository · open it in Codespaces.
Lesson notes
The rule
rolling: last w observations; expanding: all observations so far