Here is a new low-cost service called Alerts4All that offers technical trading signals for retail investors. You can, for example, have an alert sent to you every time a "Double bottom" pattern occurs.

A much more advanced version of the service will be rolled out soon -- I saw a demo today where you can backtest your strategies online, combining different fundamental and/or technical variables as entry or exit signals. They also have some built-in models for you to adapt (e.g. a model based on The Little Book that Beats the Market by Joel Greenblatt.) More interestingly, you can look at other people's trading models and their historical and/or real-time performance.

Matlab or Alphacet it is not, but I think it will be quite useful for many retail traders. It might even be useful to professional traders who want a quick-and-dirty way to test out ideas.

## Thursday, February 26, 2009

## Sunday, February 22, 2009

### Trader tax proposal will be the death knell for statistical arbitrage

U.S. Congressman Peter DeFazio, introduced H.R. 1068: “Let Wall Street Pay for Wall Street's Bailout Act of 2009”, which aims to impose a 0.25% transaction tax on the “sale and purchase of financial instruments such as stock, options, and futures.

Ladies and gentlemen, 0.25% is 50 basis points round-trip. Few if any statistical arbitrage strategies can survive this transaction tax.

And no, this is not "Wall Street paying for Wall Street's Bailout". This is small-time independent trader-entrepreneur like ourselves paying for Wall Street's Bailout.

Furthermore, this tax will drain the US market of liquidity, and ultimately will cost every investor, long or short term, a far greater transaction cost than 0.25%.

If you want to stop this insanity, please sign this online petition.

Ladies and gentlemen, 0.25% is 50 basis points round-trip. Few if any statistical arbitrage strategies can survive this transaction tax.

And no, this is not "Wall Street paying for Wall Street's Bailout". This is small-time independent trader-entrepreneur like ourselves paying for Wall Street's Bailout.

Furthermore, this tax will drain the US market of liquidity, and ultimately will cost every investor, long or short term, a far greater transaction cost than 0.25%.

If you want to stop this insanity, please sign this online petition.

## Wednesday, February 18, 2009

### Finding seasonal spreads

I am pleased to introduce guest blogger Paul Teetor for today's article.

----------

Finding Seasonal Spreads

By Paul Teetor

Finding Seasonal Spreads

By Paul Teetor

A seasonal spread is a spread which follows a regular pattern from year to year, such as generally falling in the Spring or generally rising in October. To find seasonal spreads, I've been using ANOVA, which stands for analysis of variance. ANOVA is a well-established statistical technique which, given several groups of data, will determine if the groups have different averages. Importantly, it determines if the differences are statistically significantly.

I start with several years of spread data, compute the spread's daily changes, then group the daily changes by their calendar month, giving me 12 groups. The ANOVA analysis tells me if the groups (months) have significantly different averages. If so, I know the spread is seasonal since it is consistently up in certain months and consistently down in others.

The beauty is that I can automate the process, scanning my entire database for seasonal spreads. A recent scan identified the spread between crude oil (CL) and gasoline (RB), for example. The initial ANOVA analysis indicated the CL/RB spread is very likely to be seasonal. This bar chart of each month's average daily change demonstrates the seasonality. (Click on the graph to enlarge it.)

The lines show the confidence interval for each month's average. Notice how May and June are definitely "up" months because their confidence interval is entirely positive (above the axis). Likewise, November and December are definitely "down" months. For all other months, we cannot be certain because the confidence interval crosses zero, so the true average change could be either negative or positive. The conclusion: Be long the spread during May and June; be short during November and December.

For more details, please see my on-line paper regarding ANOVA and seasonal spreads.

- Paul Teetor

I start with several years of spread data, compute the spread's daily changes, then group the daily changes by their calendar month, giving me 12 groups. The ANOVA analysis tells me if the groups (months) have significantly different averages. If so, I know the spread is seasonal since it is consistently up in certain months and consistently down in others.

The beauty is that I can automate the process, scanning my entire database for seasonal spreads. A recent scan identified the spread between crude oil (CL) and gasoline (RB), for example. The initial ANOVA analysis indicated the CL/RB spread is very likely to be seasonal. This bar chart of each month's average daily change demonstrates the seasonality. (Click on the graph to enlarge it.)

The lines show the confidence interval for each month's average. Notice how May and June are definitely "up" months because their confidence interval is entirely positive (above the axis). Likewise, November and December are definitely "down" months. For all other months, we cannot be certain because the confidence interval crosses zero, so the true average change could be either negative or positive. The conclusion: Be long the spread during May and June; be short during November and December.

For more details, please see my on-line paper regarding ANOVA and seasonal spreads.

- Paul Teetor

## Thursday, February 12, 2009

### The limitation of Sharpe ratio

Just as one should not trust VaR completely, one should also beware of high Sharpe ratio strategies. As this Economist article pointed out, a strategy may have a high Sharpe ratio because it has so far been accumulating small gains quite consistently, but it could still be subject to a large loss when black-swan events strike.

Personally, I am more comfortable with strategies that do the opposite: those that seldom generate any returns, but always earn a large profit when financial catastrophes occur.

Personally, I am more comfortable with strategies that do the opposite: those that seldom generate any returns, but always earn a large profit when financial catastrophes occur.

## Friday, February 06, 2009

### The peril of VaR

This Quebec pension fund lost some $25 billion due to non-bank asset-backed commercial paper (ABCP). Their Value-at-Risk (VaR) model did not take into account liquidity risk. As usual, the quants got the blame. But can someone tell me a better way to value risk than to run historical simulations? Can we really build risk models on disasters we have not seen before and cannot imagine will happen?

(Hat tip: Ray)

(Hat tip: Ray)

## Sunday, February 01, 2009

### Kelly formula revisited

Some discussions on Kelly's formula with a reader Steven L:

Q:

I don't have Ralph Vince's book with me, but if I recall correctly, his formulation is based on discrete bets (win or lose, no intermediate outcome), much like horse-betting or in a casino game. My approach, or rather, Professor Ed Thorp's approach, is based on continuous finance, assuming that every second, your P&L could fluctuatate in a Gaussian ("log-normal") fashion.

For discrete bets where you could have lost all of your equity in one bet, surely one should only bet a fraction of your total equity. For continuous finance, there is very little chance one could have lost all of the equity in one time period, due to the assumed log-normal distribution of prices. Hence one should bet more than your equity, i.e. use leverage.

Q:

The formula I depicted in the book is valid only if the P&L distributions are Gaussian. If one expects a fat-tailed distribution due to black-swan events, a different mathematical model needs to be used, though it can still be within the continuous finance framework. However, for simplicity's sake, if the distribution looks multinomial (e.g. high probability of "Win a lot" v "Lose a lot"), then you may model it with fractional betting just like a casino game.

Q:

"I am more than half way through your book and am stuck at a concept that I can't seem to find an answer in any other forum.A:

I have read Ralph Vince's "Portfolio Management Formulas," which uses Kelly's formula to calculate an optimal "fraction" of the bankroll to bet on each trial. So a trader can calculate a fraction of his total trading account value to risk on each trade. What I am referring to is the so-called "fixed-fractional" trading. There exists an optimal fraction that will maximize the geometric growth rate of the trading equity, in theory anyway.

However, in the money management chapter of your book, you use Kelly's formula to derive an optimal "leverage." This seems to be in conflict with what I learned from Ralph Vince, since leverage is usually great than unity and fraction is usually less than unity. I can't seem to make a connection between these two concepts. I have also seen the same optimal leverage formula in Lars Kestner's Quantitative Trading Strategies and asked the same question on some forums, but no one was able to give me a clear satisfactory answer. It would be greatly helpful if you can help me sort out the confusion."

I don't have Ralph Vince's book with me, but if I recall correctly, his formulation is based on discrete bets (win or lose, no intermediate outcome), much like horse-betting or in a casino game. My approach, or rather, Professor Ed Thorp's approach, is based on continuous finance, assuming that every second, your P&L could fluctuatate in a Gaussian ("log-normal") fashion.

For discrete bets where you could have lost all of your equity in one bet, surely one should only bet a fraction of your total equity. For continuous finance, there is very little chance one could have lost all of the equity in one time period, due to the assumed log-normal distribution of prices. Hence one should bet more than your equity, i.e. use leverage.

Q:

In example 6.2 in your book, the portfolio consists of only long SPY, which has little chance of going to zero. So I can see how it is reasonable that you use the continuous finance approach and apply the optimal leverage to scale up the return.A:

But let's assume that the portfolio consists of a single strategy that buys options. Suppose this strategy will lose most of the time due to time decay but will make profit once in a while due to black-swan events. I don't think it's a good idea to bet the entire portfolio equity on each trade for this strategy. Can you still apply the continuous finance approach in this case, since in reality trading is like making discreet bets? Should we expect the mean and variance of this strategy automatically result in an Optimal Leverage that is less than one? So that we actually need to risk a fraction of the account equity per trade?

The formula I depicted in the book is valid only if the P&L distributions are Gaussian. If one expects a fat-tailed distribution due to black-swan events, a different mathematical model needs to be used, though it can still be within the continuous finance framework. However, for simplicity's sake, if the distribution looks multinomial (e.g. high probability of "Win a lot" v "Lose a lot"), then you may model it with fractional betting just like a casino game.

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