Beta and Market-Relative Risk

/**
 * Fintech Math Bootcamp · Lesson 107 of 120
 * Beta and Market-Relative Risk
 * Module 11: Financial Risk and Performance Statistics
 *
 * Scenario: Market sensitivity is not total portfolio risk
 * Rule:     beta = Cov(asset,market)/Var(market)
 *
 * Try it:   Does beta near zero imply no risk?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

export function lesson107() {
  const market=[-.02,-.01,0,.01,.02];
  const asset=market.map(r=>1.5*r);
  const avg=(a:number[])=>a.reduce((s,x)=>s+x,0)/a.length;
  const ma=avg(asset),mm=avg(market);
  const cov=asset.reduce((s,r,i)=>s+(r-ma)*(market[i]-mm),0)/(asset.length-1);
  const varM=market.reduce((s,r)=>s+(r-mm)**2,0)/(market.length-1);
  if(varM<=0) throw new Error("Market variance must be positive");
  const result={beta:cov/varM,asset};
  return result;
}

export const checkedResult = {"beta":1.4999999999999998,"asset":[-0.03,-0.015,0,0.015,0.03]};

// Run this file directly: npx tsx lessons/11-financial-risk-and-performance-statistics/107-beta-and-market-relative-risk.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
  console.log(JSON.stringify(lesson107(), null, 2));
}

# Fintech Math Bootcamp · Lesson 107 of 120
# Beta and Market-Relative Risk
# Module 11: Financial Risk and Performance Statistics
#
# Scenario: Market sensitivity is not total portfolio risk
# Rule:     beta = Cov(asset,market)/Var(market)
#
# Try it:   Does beta near zero imply no risk?
#
# Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
# Free course:    https://courses.thefintechbuilder.com
# Synthetic teaching example, not financial advice or a production library.
#
# Run: python main.py

import json


def lesson_107():
    market = [-0.02, -0.01, 0.0, 0.01, 0.02]
    asset = [1.5 * r for r in market]

    def avg(a):
        total = 0.0
        for x in a:
            total += x
        return total / len(a)

    ma, mm = avg(asset), avg(market)
    cov = 0.0
    for i, r in enumerate(asset):
        cov += (r - ma) * (market[i] - mm)
    cov /= len(asset) - 1
    var_m = 0.0
    for r in market:
        var_m += (r - mm) ** 2
    var_m /= len(market) - 1
    if var_m <= 0:
        raise ValueError("Market variance must be positive")
    return {"beta": cov / var_m, "asset": asset}


if __name__ == "__main__":
    print(json.dumps(lesson_107(), indent=2))

/*
 * Fintech Math Bootcamp - Lesson 107 of 120
 * Beta and Market-Relative Risk
 * Module 11: Financial Risk and Performance Statistics
 *
 * Scenario: Market sensitivity is not total portfolio risk
 * Rule:     beta = Cov(asset,market)/Var(market)
 *
 * Try it:   Does beta near zero imply no risk?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
 * 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) {
        double s = 0;
        for (double x : a) s += x;
        return s / a.length;
    }

    static Map<String, Object> lesson107() {
        double[] market = {-.02, -.01, 0, .01, .02};
        double[] asset = new double[market.length];
        for (int i = 0; i < market.length; i++) asset[i] = 1.5 * market[i];
        double ma = avg(asset), mm = avg(market);
        double cov = 0, varM = 0;
        for (int i = 0; i < asset.length; i++) cov += (asset[i] - ma) * (market[i] - mm);
        cov /= asset.length - 1;
        for (double r : market) varM += Math.pow(r - mm, 2);
        varM /= market.length - 1;
        if (varM <= 0) throw new IllegalArgumentException("Market variance must be positive");
        return obj("beta", cov / varM, "asset", list(asset));
    }

    public static void main(String[] args) {
        System.out.println(toJson(lesson107(), ""));
    }

    // 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;
    }
}

// Fintech Math Bootcamp · Lesson 107 of 120
// Beta and Market-Relative Risk
// Module 11: Financial Risk and Performance Statistics
//
// Scenario: Market sensitivity is not total portfolio risk
// Rule:     beta = Cov(asset,market)/Var(market)
//
// Try it:   Does beta near zero imply no risk?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
// 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"
	"math"
)

type Result struct {
	Beta  float64   `json:"beta"`
	Asset []float64 `json:"asset"`
}

func avg(a []float64) float64 {
	s := 0.0
	for _, x := range a {
		s += x
	}
	return s / float64(len(a))
}

func lesson107() Result {
	market := []float64{-.02, -.01, 0, .01, .02}
	asset := make([]float64, len(market))
	for i, r := range market {
		asset[i] = 1.5 * r
	}
	ma, mm := avg(asset), avg(market)
	cov, varM := 0.0, 0.0
	for i, r := range asset {
		cov += (r - ma) * (market[i] - mm)
	}
	cov /= float64(len(asset) - 1)
	for _, r := range market {
		varM += math.Pow(r-mm, 2)
	}
	varM /= float64(len(market) - 1)
	if varM <= 0 {
		panic("Market variance must be positive")
	}
	return Result{Beta: cov / varM, Asset: asset}
}

func main() {
	out, err := json.MarshalIndent(lesson107(), "", "  ")
	if err != nil {
		panic(err)
	}
	fmt.Println(string(out))
}

// Fintech Math Bootcamp · Lesson 107 of 120
// Beta and Market-Relative Risk
// Module 11: Financial Risk and Performance Statistics
//
// Scenario: Market sensitivity is not total portfolio risk
// Rule:     beta = Cov(asset,market)/Var(market)
//
// Try it:   Does beta near zero imply no risk?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
// 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) {
    double s = 0;
    for (double x : a) s += x;
    return s / static_cast<double>(a.size());
}

Json lesson107() {
    const std::vector<double> market{-.02, -.01, 0, .01, .02};
    std::vector<double> asset;
    for (double r : market) asset.push_back(1.5 * r);
    const double ma = avg(asset), mm = avg(market);
    double cov = 0, varM = 0;
    for (std::size_t i = 0; i < asset.size(); ++i) cov += (asset[i] - ma) * (market[i] - mm);
    cov /= static_cast<double>(asset.size() - 1);
    for (double r : market) varM += std::pow(r - mm, 2);
    varM /= static_cast<double>(market.size() - 1);
    if (varM <= 0) throw std::invalid_argument("Market variance must be positive");
    return jobj({{"beta", jnum(cov / varM)}, {"asset", jnums(asset)}});
}

int main() {
    writeJson(std::cout, lesson107());
    std::cout << "\n";
    return 0;
}

// Fintech Math Bootcamp · Lesson 107 of 120
// Beta and Market-Relative Risk
// Module 11: Financial Risk and Performance Statistics
//
// Scenario: Market sensitivity is not total portfolio risk
// Rule:     beta = Cov(asset,market)/Var(market)
//
// Try it:   Does beta near zero imply no risk?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
// 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(&quote(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(&quote(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, x| s + x) / a.len() as f64
}

fn lesson_107() -> Json {
    let market: [f64; 5] = [-0.02, -0.01, 0.0, 0.01, 0.02];
    let asset: Vec<f64> = market.iter().map(|r| 1.5 * r).collect();
    let (ma, mm) = (avg(&asset), avg(&market));
    let cov = asset.iter().zip(market.iter()).fold(0.0, |s, (r, m)| s + (r - ma) * (m - mm))
        / (asset.len() - 1) as f64;
    let var_m = market.iter().fold(0.0, |s, r| s + (r - mm).powi(2)) / (market.len() - 1) as f64;
    assert!(var_m > 0.0, "Market variance must be positive");
    obj(vec![("beta", Json::Num(cov / var_m)), ("asset", nums(&asset))])
}

fn main() {
    let mut out = String::new();
    write_json(&lesson_107(), "", &mut out);
    println!("{}", out);
}

// Fintech Math Bootcamp · Lesson 107 of 120
// Beta and Market-Relative Risk
// Module 11: Financial Risk and Performance Statistics
//
// Scenario: Market sensitivity is not total portfolio risk
// Rule:     beta = Cov(asset,market)/Var(market)
//
// Try it:   Does beta near zero imply no risk?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/financial-risk-and-performance-statistics/beta-and-market-relative-risk/
// 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(Lesson107(), options));

static object Lesson107()
{
    double[] market = { -.02, -.01, 0, .01, .02 };
    double[] asset = market.Select(r => 1.5 * r).ToArray();
    double Avg(double[] a) => a.Aggregate(0.0, (s, x) => s + x) / a.Length;
    double ma = Avg(asset), mm = Avg(market);
    double cov = asset.Select((r, i) => (r, i)).Aggregate(0.0, (s, p) => s + (p.r - ma) * (market[p.i] - mm)) / (asset.Length - 1);
    double varM = market.Aggregate(0.0, (s, r) => s + Math.Pow(r - mm, 2)) / (market.Length - 1);
    if (varM <= 0) throw new InvalidOperationException("Market variance must be positive");
    return new { beta = cov / varM, asset };
}