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
This is a Berkshire Hathaway trading strategy which would have given almost ten times the return performance of buy/hold over the last 10 years with half the drawdown. The strategy is detailed in the table below, it was straightforward, with 123 trades over the 10 year backtest period. All trading was done at the weekly […]
Here is a Google Inc (GOOGL) trading strategy with once a week intervention which would have performed significantly better than buy-hold over the last 10 years. Annualized return was 31.5% vs. 16.5% (returning $149K for 10K outlay vs $36.7K, compounded), drawdown was 40.2% vs. 62.4%, so reward/risk was better. The buy and cover signal (see […]
Here is a Walt Disney Company (DIS) trading strategy with daily maintenance which had quite nice characteristics; 59% annualized return over the last 2 years. The performance was better than long buy-hold with lower drawdown and about three times the reward-risk. Signal reinforcement was good, and not many dual signal days. Below I show the […]
GLD is the much traded SPDR Gold Trust ETF. I find these two GLD trading strategies interesting because they gave reasonable results (32.6% and 48% annualized return) for each of the four 6 month periods of the analysis. The strategies require daily intervention. Strategy 1: BCS AHC This is a buy on fall, sell on […]
This TSLA trading strategy would have given a 1062% return over 2.1 years vs. a buy-hold return of 86% for the same period. The strategy is based on buying and selling when the stock price rises above specific thresholds. The buy side keyed off the day’s open price; the buy and cover signal appeared when […]
Today I present two TQQQ trading strategy backtest results with weekly setup with similar reward-risk but very different characteristics. TQQQ is the ProShares UltraPro QQQ, a triple leveraged ETF tracking the Nasdaq. The backtests were for the 288 weeks 2/11/10 through 8/14/15. The first trading strategy was found by optimizing the scanner for low drawdown, […]
Frequent reversals characterize this strategy for Omeros Corporation This OMER trading strategy is signal rich; there were 174 dual signal days out of the 528 days in the analysis. Added to that 151 buy signal only days and 39 sell signal only days and you get 364 signal days, of which 250 were actionable signals […]
This is an interesting AAPL trading strategy for the period 12/12/80 through 7/31/15, which would have generated $8,521,705,763 in profits from $10,000 initial investment. Buy and hold would have generated $2,744,894 in profit in the same period. The cover and buy signals were generated when the stock price dropped 37.77% below the 20 month exponential […]