We develop statistically validated trading strategies that can be used by institutional and individual investors to help aide in the decision making process. Developers of this website are former institutional brokers. We have transacted billions of dollars in stocks, bonds, options and earned millions in commission dollars from some of the most successful money managers and hedge funds in the world. We are primarily market technicians and have been schooled in fundamental analysis via our institutional research sales background and quantitative stock screening analysis. Our work has been published on the Kirk Report,TraderFeed, Ezine, Ehow, Tipd and Feed the bull. Our pledge is to establish lasting relationships with our customers by exceeding their expectations and gaining their trust through exceptional performance by every member of our team. We are committed to providing the best and latest product offerings for online purchase. If you have any questions about our new products please do not hesitate to contact us.
What we do:
We use computer-based models to predict price changes in liquid easily-traded financial instruments. Our models are based on analyzing as much data as is available, then looking for non-random movements to make predictions. Our market timing models have historically outperformed the indices by large margins with significantly less volatility, in back tests and real time. Our signals are concise and actionable there is no ambiguity.
Why trade a quantitative system?
A computer can tell us exactly what the markets have done in the past. Computers don’t sleep, don’t get tired, don’t get sick, don’t care about politics or fundamentals and don’t vacation in late August in the Hamptons. It doesn’t matter how many years of experience a person has they will not be able to remember the past precisely, like a machine . There are several studies that have proven quantitative models have better hit rates than an expert opinion, here is one example.
The best performing fund that I know of is Renaissance Technologies , this fund is quantitatively driven and uses computer systems to trade the markets. Renaissance Technology has averaged 35% annual returns since 1989 AFTER their 44% performance fee and 5% management fee. This means that Renaissance Technology fund has managed to perform about 68% annually before fees, since 1989. This is a strategy I want to emulate, how about you?
The idea is to plan your trades based on historical tendency’s outside of market hours, when you have a clear head. This preparation helps you make sound decisions during chaotic market environments. Plan the trade and trade the plan.
Anatomy of a trading system:
This section is to help you understand the basics of what goes into developing the systems that are displayed on this website.
1)First we devise a logical hypothesis. We will only test and trade an idea that is logical and can be quantified. For example buying energy stocks after a decline while crude oil or gasoline has risen makes sense and might be researched. However buying energy stocks if a completely unrelated market such as pork bellies has risen doesn’t make sense and I will not research.
2) Code and test the entry and exit concepts using default values. Optimize the default values to find the performance results on a given segment of data (this piece of data should have both bullish and bearish periods).
3) Evaluate the systems and choose the system from the optimization report that has the most linear equity curve, highest profit factor and smallest drawdown.
4) Test the system on a segment of data that was not used. If the system performs well on the new data we may be working with valuable indicators that might perform well in real time.
5) If we have found a concept that we believe adds value, start trading the system with a small position size. Absolutely take every entry and exit signal. We don’t use subjective opinions in a backtest to decide which signals to take or not take, so we must not add our opinions in real time. Every time I have added my personal opinion into a completely objective system I’ve always missed the big winners and been involved for the losing trades. The computer doesn’t get distracted by phone calls or co workers and the computer always thinks clearly. The main point is that a lot of thought and research went into these systems while not under the pressure of having capital at risk. If capital is at risk it clouds our judgment with the emotion of making or losing money, this is a bad time to make very important decisions.
6) Analyze the performance of the realtime PnL compared to the performance of the system. If you are underperforming the system significantly then you need to make adjustments. Trade slippage is a big issue. I like to use stop limit entries as a good way to replicate the exact entry of the system.
7) Monitor the equity curve of the trading system. If the equity curve is advancing from bottom left to the top right of your screen, your system is making money. I like to use a moving average on the equity curve and only trade the system when it is above that moving average. I will stop trading a system if it falls below a smoothing mechanism. We call this system failsafe a “system circuit breaker” Sometimes markets change and systems stop working, so we will only trade the systems that are working in the current market environment.
As an example:
Step 1) Hypothesis
For this example we will test the idea of a moving average cross over system. A lot of the technical analysis books talk about using this technique so it must work, right? HMMM… Lets explore, and let the markets history reveal the truth to us.
Step 2) program and test the rules
In testing on the S&P futures I will start with a 20 day ma crossing above a 50 day ma for buys and a 20 day ma crossing below a 50 day ma for shorts. This System test will either be long or short this is what’s referred to as a stop and reverse system it is always in the market either long or short.
This is what the rules, coded will look like
Buy Rule:
IF MovingAvg ( Close, 20) > MovingAvg ( Close, 50) THEN BUY at Market
Sell Rule:
IF MovingAvg ( Close, 20) < MovingAvg ( Close, 50) THEN SHORT at Market
Now that the programing code has been written I can test the concept! Below are the results of trading one S&P futures contract based on the above rules since 1982.
You can see that this strategy of buying or selling based on a 20 day cross of the 50 day moving average does not work very well. More precisely this 20/50 day concept lost -$273k since 1982 and as a comparison a buy and hold approach would have made $175k. The initial thought was wrong but lets explore a little further and test different periods of moving averages to see if maybe the 20 and 50 day period was just bad values to start with. For the short moving average I will test periods 1 – 25 and for the long term moving average I will test periods 25-50 I will step in increments of 1 and test all combinations of buys and sells. There will be a total of 650 different systems tested. See the optimization report below
I can now sort my optimization by performance metric. The optimization report seen above, indicates that the best combination of moving averages based on highest profit factor and highest net profit is a 16 day and 25 day moving average cross. However, Out of the 625 systems tested only 42 returned a profit. The best systems total net profit was $190,875 the worst systems total net profit is -$331,725. I will also note that the highest average trade for the systems tested was $592 per contract and the average holding period was 24 trading days. As a comparison a buy and hold approach would have returned $175k if I bought the S&P at random and held for 24 trading days the average return would be $1,348.I will halt this example at step 3 because in my opinion this concept of trading a short tern moving average cross over in the S&P is a losers game and only one out of the 650 systems tested out performed buy and hold!
This was a very basic explanation of how and why we develop a trading system.