Algorithm Science offers you a spreadsheet for backtesting stock price data. Our PriceDelta tool helps the investor discover swing trading strategies for stocks. If there are hidden patterns in the stock price data caused by large institutional automatic trading, PriceDelta can expose them and find which daily stock price movements would have generated the most profitable exploits. A PriceDelta trading strategy looks like this:
|SIGNAL A TRADE IF THE PRICE HITS A FIXED PERCENTAGE ABOVE OR BELOW A REFERENCE PRICE
The reference price can be the open, close, high, low, a moving average or a user defined price, and it changes prior to every trading session. The fixed percentage is called the buy-point or sell-point. Most PriceDelta strategies require entering orders just once a day or less, so they are easy to implement either manually or automatically. The tool can't tell you which strategies will profit in the future but it can provide you a window into what patterns would have worked over the recent past.
Type in a stock symbol see exchanges and PriceDelta imports one year of daily price data from a reliable internet data provider. PriceDelta then back-tests and graphs the performance of eight trading systems of your choosing each with a hundred buy-sell points side-by-side so you can easily compare and see which would have performed best. See screenshot. There are hundreds of different algorithms you can configure and test see them. PriceDelta graphs show buy/sell signal and trading dates, returns, efficiencies, drawdowns and reward/risk ratios. You can also tweak the strategies for further improvements. In the sumulations, positions are held for anything from one day to several months. Signals are clear, unambiguous and easy to understand. Here's a typical example of what you can uncover:
This is a growth graph of a simple trading strategy for SOXL found by PriceDelta showing $10,000 growing into $221,322 in just over 1 year. The trading system worked as follows; on day 1 you bought long if and when the price reached 0.7% above the open price. Next day you reversed to short if and when the price hit 0.7% below the open. You continued to alternate between long and short positions this way. If the price didn't meet the 0.7% target on any day, you would hold the position and wait until the next day, changing the target at the open as usual. This particular strategy would have yielded 2113% compounded returns over a one year period less trading costs.
We've published almost a hundred algorithms and tracked their returns after publication. Those returns covered a huge range (from negative 75% to positive 450%) but on average the group yielded over 50% compounded returns--less trading fees--over a 9 month period see details. Returns were expectedly less than we found in the simulations, but they were much better than the market, and 66% better than the return on the underlying stocks.
There's many more trading strategy examples on this site see them. You can also learn more about how to use the tool by watching the videos see video, going through the tutorial see tutorial, or browsing the blog see blog.
Thank you for visiting and happy strategizing!