Historical Data

There are numerous sites offering various levels of quality historical stock price data. Below you can find some common sources of free historical data. Google is also your friend here, there’s lots of datasources out there.

Our aim will be to minimize costs

Related posts:

• Algo-Trading 101 Part 1: Getting Started
• Thursday, March 11
• System Trading
• Tuesday, December 22
• Monday, March 15

What this post will aim to do is explore the journey of the trading business startup, from finding a viable trading strategy, backtesting it for verification on historical performance, setting up the tech-infrastructure (low-cost!) and building and deploying an automated trading system (ATS) to execute your strategy.

Other than that we’ll be looking at using Matlab or using its cheaper alternative, R for the backtesting of some quantitative trading strategies.

Being proficient at basic stats and basic programming will definitely help, but you won’t need a PhD to put the theories into practice. Strategies will mostly be of the quantitative nature e.g. statistical arbitrage, and the aim is to keep things simple for now. StatArb deals with the simplest financial instruments: stocks, futures and sometimes currencies.

Let’s look at the process of developing a successful trading strategies and putting it into action!

Finding A Quantitative Strategy That Works (And Suits You!)

Unlike popular opinion would suggest, many strategies are publicly available and while they may not be super-profitable can be tweaked to become profitable.

The key is to backtest strategies for historical performance, thereby verifying that they represent a sound hypothesis. While this alone does not guarantee a profitable strategy, it is certainly a very good indication on the future success of a strategy.

Key things to consider when deciding on a suitable strategy are:

• Available time: If you’re working full time and only able to trade part time, you probably don’t want to trade intraday strategies but rather stick to ones that trade overnight. Quantitative trading lends itself well to automation in this regard, since the computer can handle most things automatically, if set up correctly.
• How programmatically inclined you are: Knowing some languages like Java, C# or C++ will definitely be a huge plus when it comes to algo-trading (after all its not called ‘algorithmic trading’ for nothing – trading algorithms are just computer programs firing off buy and sell orders to the markets.
• Trading capital: Depending on your level of available capital, you may not be in a position to trading algos in the first place. Your capital will determine alot of things such as what type of accounts you’ll be setting up, brokerage discounts (based on volume traded), etc, etc. Generally, the more you have to begin trading with, the better.
• Does it have a high Sharpe ratio?
• Does it outperform a benchmark? (Say the ASX/S&P200 Index)
• Does it have small enough drawdown and short enough drawdown duration?
• Does the strategy’s historical performance start declining towards the more recent years compared to that of the earlier years?

Related posts:

• Algo-Trading 101 Part 2: Backtesting
• Thursday, March 11
• Australian Market – Wednesday, March 17
• Tuesday, December 22
• Monday, March 15

Las Vegas. Great food, show girls, and a multi-billion dollar gambling
business. The money made by the casinos is only matched by the profits
on Wall Street. And the profits of both are based on mathematical probabilities.

What does this have to do with trading systems?

• We want the odds of a trade to favor us – We want a positive expectancy
• We want a lot of trades – opportunity
• We want turn over so we can compound the profits – holding time.

What we as traders must do is become the house. The odds in our trading
must favor us, we need a reasonable number of trades during the year
and the trades must be completed in a reasonable amount of time for
compounding to be effective.

Expectancy is simply the product of your profit percentage per win and
your win rate minus the product of your loss percentage per loss and
your loss rate, or:

Expectancy, E = (Ave. Win Size * % Wins) – (Ave. Lose Size * %
Losers)

For example, if we have the results of backtesting a purely
technical system, with the following results:

• Win percentage 6%
• Win rate 60%
• Loss percentage 4%
• Loss rate 40%

The expectancy is 2.0% per trade, or (6% x 60%) – (4% x 40%).

That means, on an average trade, 2% of the money traded is yours to
keep. That’s better odds than a casino gets on blackjack. Now,
that may not sound like a lot of money. If your average trade is $10,000
– 2% is $200 profit per trade. If you have 300 trades per year,
then you have a $60,000 profit per year with an average trade of only
$10,000. This does not even include the profits if you compounded
the average trade.

If you explore the expectancy formula, you will notice that there is
no one set of numbers that could give a positive expectancy but an infinite
number of sets and therefore an infinite number of trading systems that
could be profitable.

Given that, it is possible to develop systems where the stop-loss is
larger than the profits. The stop-loss is academic, as long as your
profit expectancy is positive.

Here’s another example: we could use a 20% stop loss and a 5%
profit target and come out with the same exact 2% expectancy as long
as my win rate is high enough! An 88% win rate in this example would
yield 2.0%, the result of (5% x 88%) – (20% x 12%).

Or, you could arrive at a positive expectancy with a very low win rate.
One of the more famous expectancy numbers comes from William O’Neil,
advocate of the CANSLIM system and founder of Investors Business Daily.
If we use his stop and target numbers of 8% and 20% and his published
win rate of 30%, the expectancy can be calculated to be: (20% x 30%)
- (8% x 70%) or +0.4%.

The bottom line is: expectancy must be positive if you want to make
a profit over time. Never use a system with a zero or negative expectancy.
You will not win. You cannot beat the house over a long series of bets
or trades. Be the “House”.

No matter what your expectancy is, you will not make a great deal of
money unless you have a lot of opportunities to trade. Again, the casino
analogy. The casino may only make 1-2% per hand of blackjack, but they
turn over those hands very quickly – 30 to 40 hands per hour.
Play blackjack long enough and you will lose over 40% of your money
per hour! No wonder they can offer those wonderful comps.

The most overlooked area of trading is the “holding period.”
In order to make money, you must have a system that generates a positive
expectancy and a lot of opportunities. But you must have access
to your money. If your trade’s hold time is too long, you
can’t take advantage of all or even most of your opportunities. Your
trading money or buying power is always tied up because you have to
wait too long to close your trades.

Casino analogy time. If the house odds in Blackjack are 2.5%, that means
for ever $2 bet, the casino makes, on average, 5 cents. If you only
play 1 game per hour, the casino makes 5 cents per hour. If you play
60 games per hour, the casino has all of your $2 in 40 minutes. All
things being equal, the game with the fastest turnover is the more profitable
for the casino.

It’s no different with trading. You will be more profitable with $100,000
that you could “turn” 250 times per year, than $500,000 that
was tied up in one trade for 12 months. As an example, let’s say we
have one trade and that trade yielded a 50% return. You just had a great
year – a $250,000 profit.

On the other hand, say you had $100,000 for stock purchases, and your
expectancy was only 1.2% per trade but you turned over your stocks 250
times in the same year. This method ends up generating $300,000 for
the year, and that assumes you never increase the position size as the
equity grows. You just had a better year. And it is easier to get 1.2%
per trade than 50%.

The bottom line for a great bottom line is:

• A positive expectancy
• A good number of trades
• A short holding period

Related posts:

• Algo-Trading 101 Part 1: Getting Started
• Algo-Trading 101 Part 2: Backtesting
• Handy Linux Commands
• CFD Basics
• Options 5: Why Trade Options?