close, reduces variance by 1 divided by root 2 = 71%
Yeah, that's it, thanks Visaria.
What is your strategy?
- Thread starter kut2k2
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Yeah, that's it, thanks Visaria.
The point of Kelly is to find the optimal fraction of bankroll to use, variance doesn't come into it unless you can't take the heat. In this situation, we know precisely the probabilities of each outcome and only have a thousand dollars at risk. So no need to scale down Kelly.
Right, that's what I thought, too, but after running the Monte-Carlo simulation on this problem, the results are totally not what I expected. The top strategy is this combo: {R16:60%, R14:30%, Red:10%}. So, it's a bet of 100% of the bankroll on every bet that maximizes the average profit over many trials. The question is, why so reckless? Well, if you think about it, this strategy has an absolutely enormous variance. Essentially, the result is either an almost certain total loss of $1,000, or a very small chance of a huge gain (in the order of $10 million after the 10 spins of the wheel). When these outcomes are averaged, the average is still in the millions with this full allocation.
I am trying to think about this problem as applicable to position sizing in trading, and clearly, no one in their right mind would risk 100% of the capital on every trade. So, I do think that the adjustment for volatility is needed.
As an experiment, I added a heavy penalty to the strategies which result in the drawdown of more than 50% during the run, and with that penalty in place, the top strategy was a much more believable {R16:7%, R14:0%, Red:0%}
I'll post my code and the results later.
Ok, here is the Java code for the Monte-Carlo simulation. It creates 176851 strategies with the different allocation of capital to R16, R14, and Red, and then spins the wheel 10 times. This counts as one trial. At the end of the trial, the profit is calculated for each of the 176851 strategies. Then this trial is repeated 1000 times, and the average profit for each strategy is calculated. At the end, it prints 3 sets of results, based on the maximum allowed drawdown.
Code:
package com.et.portfolio;
import java.util.*;
public class Portfolio {
private final static double startEquity = 1000;
private static int maxAllowedDrawdown; // as percent
private final static long numberOfSpins = 10L;
private final static long numberOfTrials = 1000L;
private final static double oneHundred = 100d;
public static void main(String[] args) {
Portfolio portfolio = new Portfolio();
maxAllowedDrawdown = 25;
portfolio.test();
maxAllowedDrawdown = 50;
portfolio.test();
maxAllowedDrawdown = 75;
portfolio.test();
}
class Strategy implements Comparable<Strategy> {
private final int betR16, betR14, betRed;
private double equity, averageProfit, equityPeak;
private long trials;
Strategy(int betR16, int betR14, int betRed) {
this.betR16 = betR16;
this.betR14 = betR14;
this.betRed = betRed;
equity = equityPeak = startEquity;
}
private void update(int outcome) {
if (outcome >= 1 && outcome <= 4) { // R-16
equity += 35 * equity * (betR16 / oneHundred);
}
if (outcome >= 5 && outcome <= 7) { // R-14
equity += 35 * equity * (betR14 / oneHundred);
}
if (outcome >= 1 && outcome <= 23) { // red
equity += 1 * equity * (betRed / oneHundred);
}
if (outcome >= 24 && outcome <= 37) { // anything other than red-16, red-14, or red
equity -= equity * (betR16 + betR14 + betRed) / oneHundred;
}
if (equity > equityPeak) {
equityPeak = equity;
}
double drawdown = 100 * (1 - (equity / equityPeak));
if (drawdown >= maxAllowedDrawdown) {
averageProfit = Double.NEGATIVE_INFINITY; // heavy penalty
}
}
private void update() {
averageProfit += (equity - startEquity);
equity = equityPeak = startEquity;
trials++;
}
public long getAverageProfit() {
return (long) (averageProfit / trials);
}
public double getEquity() {
return equity;
}
@Override
public int compareTo(Strategy other) {
return Long.valueOf(other.getAverageProfit()).compareTo(getAverageProfit());
}
@Override
public String toString() {
String line = "\t" + betR16 + "\t" + betR14 + "\t" + betRed + "\t" + getAverageProfit();
return line;
}
}
private void test() {
int min = 1;
int max = 37;
List<Strategy> strategies = new ArrayList<>();
int maxPercent = 100;
for (int betR16 = 0; betR16 <= maxPercent; betR16++) {
for (int betR14 = 0; betR14 <= maxPercent; betR14++) {
for (int betRed = 0; betRed <= maxPercent; betRed++) {
if (betR16 + betR14 + betRed <= maxPercent) {
Strategy s = new Strategy(betR16, betR14, betRed);
strategies.add(s);
}
}
}
}
System.out.println("total strategies: " + strategies.size());
Random random = new Random();
for (int trial = 1; trial <= numberOfTrials; trial++) {
for (int spin = 1; spin <= numberOfSpins; spin++) {
int outcome = random.nextInt(max + 1 - min) + min;
if (outcome < 1 || outcome > 37) {
throw new RuntimeException("unexpected outcome: " + outcome);
}
for (Strategy s : strategies) {
s.update(outcome);
}
}
for (Strategy s : strategies) {
s.update();
}
}
Collections.sort(strategies); // sort from the best to worst
int count = 0;
String line = "Max allowed drawdown: " + maxAllowedDrawdown;
System.out.println(line);
line = "\t" + "R16" + "\t" + "R14" + "\t" + "Red" + "\t" + "AveProfit";
System.out.println(line);
for (Strategy s : strategies) {
System.out.println(s);
count++;
if (count >= 10) { // only show the top 10 strategies
break;
}
}
}
}And here are the results, which are the top 10 strategies, for each of the max allowed drawdown:
Note that the theoretically calculated combo {R16:8, R14:0, Red:0), which both Visaria and I calculated, comes very close to the top strategy from the Monte-Carlo simulation, at 50% max drawdown: {R16:7, R14:0, Red:0).
Code:
Max allowed drawdown: 25
R16 R14 Red AveProfit
3 0 0 1609
2 1 0 1449
1 2 0 1224
2 0 2 1053
2 0 1 995
0 3 0 953
2 0 0 938
1 1 2 885
1 1 1 833
1 1 0 781
Max allowed drawdown: 50
R16 R14 Red AveProfit
7 0 0 6788
6 1 0 6485
5 2 0 6117
4 3 0 5746
3 4 0 5427
0 7 0 5405
6 0 1 5287
2 5 0 5218
1 6 0 5185
6 0 0 5044
Max allowed drawdown: 75
R16 R14 Red AveProfit
12 2 0 44417
13 1 0 44277
11 3 0 43200
14 0 0 42618
10 4 0 40948
9 5 0 38051
12 1 1 37303
11 2 1 36965
13 0 1 36365
12 1 0 35588Note that the theoretically calculated combo {R16:8, R14:0, Red:0), which both Visaria and I calculated, comes very close to the top strategy from the Monte-Carlo simulation, at 50% max drawdown: {R16:7, R14:0, Red:0).
Thanks for demonstrating why I have no use for MCS. There is no optimal strategy that ever risks total loss like the one above does. The first time the ball lands on a black number or the green number, you're done. That's nowhere near optimality.
I believe that the above Monte-Carlo simulation results are totally valid. In the extreme case of a 100% allocation, they simply show that if you are willing to blow your account thousands of times, eventually you would make enough to recoup all the losses. In other words, it assumes that you can play this game an infinite number of times, and that you have a never ending source of the new capital. Of course, no trader can and should operate this way, and this is where I believe the risk-adjustment should be made, either in the form of the max acceptable drawdown, or even better, the volatility of the final bankroll. I will make that adjustment tomorrow, and re-run the simulation.
When was the last time the law of large number was applicable to small sample-sets, 10 or even 1 as you suggest?
I welcome the proof here that the Kelly fraction is the way to bet on a 1-spin limit. I will certainly learn something and have no shame about it.
Here are some MC results:
8%-bankroll Red-16 : ending bankroll 50th percentile +1823, stdev 68626
4%-bankroll Red-16 : ending bankroll 50th percentile +1682, stdev 7463
$50 fixed bet Red-16: ending bankroll 50th percentile +2350, stdev 1821
Those 3 strategies guarantee a minimum ending bankroll of +438, +669, +500 respectively.
6.7%-bankroll Red-16 is the one fractional strategy that guarantees a minimum ending bankroll of +503, ie. similar to fixed bet $50. Its results are:
ending bankroll 50th percentile +1821, stdev 22539
Now do you still think Kelly applies to small sample-sets?
8%-bankroll Red-16 : ending bankroll 50th percentile +1823, stdev 68626
4%-bankroll Red-16 : ending bankroll 50th percentile +1682, stdev 7463
$50 fixed bet Red-16: ending bankroll 50th percentile +2350, stdev 1821
Those 3 strategies guarantee a minimum ending bankroll of +438, +669, +500 respectively.
6.7%-bankroll Red-16 is the one fractional strategy that guarantees a minimum ending bankroll of +503, ie. similar to fixed bet $50. Its results are:
ending bankroll 50th percentile +1821, stdev 22539
Now do you still think Kelly applies to small sample-sets?
The Kelly fraction is the optimal geometric growth fraction. It by definition is the betting strategy that will grow your bankroll the fastest for a given trading strategy. If you had only a single spin, which better betting strategy would you use instead of Kelly?
Remember: we have yet to determine what the best trading strategy is for this roulette example, which boils down to which bets to take. It appears that incorporating Kelly is a necessary component to deciding what will be the best trading strategy.
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