Excel Based Tools and Services
At Algorithm Science we offer a variety of tools for gathering data from the internet and analyzing it. Here, you can find tools for extracting data from JSON (Java Script Object Notation) streams which is rapidly evolving as the new data standard on the web. We have JSON extractors for all kinds of financial information including historical stock price data, balance sheets, option chains, portfolio information. We also have a tool for searching news. These tools use a framework called JeX which greatly simplifies the process for JSON extraction and we are pleased to offer the JeX framework as a technological innovation which our customers can use to do their own JSON extraction.
Here, you can also find SignalSolver, our flagship product, which back-tests price history and exposes buy and sell signals which would have exploited these price movements. From time to time, we publish the signals we have found, and a few are presented in the sections below.
In support of legacy financial spreadsheets, we also offer EmulateURL, a product which emulates the now defunct Yahoo! Finance URLs which provided historical price, dividend and split data, consequently we are able to give such spreadsheets new life.
SIGNALS POSTED RECENTLY
Original Post March 12th 2015Please note, this post was corrected 1-3-16 to account for a short-side error in the original calculations. Apologies for this. This NUGT trading strategy (Direxion Daily Gold Miners Bull 3X ETF) gave a theoretical $1.8 million profit for a $10K initial investment over 2.1 years. The trading strategy required once a […]
With $94 billion in profits (for $10K initial investment) this has one of the highest total returns I have seen for any algorithm. It is an interesting study, but I wouldn’t recommend it as a good system to trade moving forward. It is somewhat over-tuned, and the parameters are close to regions which would have […]
This is a GILD trading strategy with weekly intervention required. I found algorithms with better returns, but they were less consistent over time and had more parameter sensitivity. I chose this one because of many favorable characteristics:- Buy low, sell high type of algorithm, biased towards buying as befitting an upward trending stock like GILD. […]
This Palo Alto Networks PANW trading strategy would have given a 119% annualized return. Here, I chose to show only the long side of the algorithm because its more impressive than the short side (which only gave 27% annualized return). To appreciate the algorithm more, notice that the efficiency of the algorithm was close to 200%. […]
Algorithm for GOOGL based on monthly OHLC data and requiring monthly setup. Buy signals around 3 times more frequent than sell signals, some overlap (dual signal months shown as white lines). 80% long, 20% short. Algorithm showing flattening out of performance in recent months.GOOGL.M Performance table and strategy description. Showed about 3 times the reward-risk […]
Here is a strategy that worked on MSFT for the last 2 years. It gave returns around five times better than buy-hold, a total return of 360% vs. 70% for buy-hold. Drawdown was 10.6% vs. 17.9% for buy-hold. Backtest results are shown in the graphs below. MSFT daily trading strategy MSFT daily trading strategy, performance […]
Shows the results of backtesting a trading strategy for the stock SPY (SPDR S&P 500 ETF) over the 10 year period 1/18/05 to 2/23/15. This was a weekly strategy meaning that it needed setting up once a week, and it traded once a week or less. I chose this strategy because it gave the best […]
Micron Technologies monthly trading strategy, 46% Annualized Return, 25 yearsPlease note, this post was edited Jan 6th 2016 to correct an error in the short-side calculations. The original post showed an annualized return of 57%, which was erroneous. Below are shown the corrected results for this algorithm. Micron Technology (MU) trading strategy base on monthly […]