MSFT Trading Strategy (Daily)

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 table and strategy details. Note that "user defined price" is the average of the high and low of previous day and the open of current day [ (h+l+o)/3 ]. A few observations; return and reward/risk was 5x better than buy-hold. Drawdown of 10.6% vs. 17.9% for buy-hold. Long side worked much better than short side.

MSFT daily strategy, graph showing how return varied with buy and sell point parameters for different time periods. The blue line shows the overall 2 year graphs, the others are for 6 month periods. All periods gave better returns than buy-hold. Note that buy point range is fairly restricted.

MSFT daily strategy, surface plot of return (z axis) as it changes with buy and sell point. The blue plateau is roughly the same return as buy-hold.

On Jan-5-2016 this post was corrected for a short-side return error, and a commission of $7 per trade was factored in.

Update Jan 5th 2016

This algorithm was not a stellar performer. Since publication it has been essentially flat, let down badly by the short-side performance

MSFD-D Table Update Jan-5-2016

To be fair, the long side performance was reasonable, but buy-hold was on a roll.

Optimum parameters for this period were different than the original posting, buy point -0.98%, sell point 10.33% for this result:

MSFD-D Table Update Jan-5-2016 Optima

Again, long-side was much more interesting than short-side performance.

Update Oct 19th 2016

This algorithm continues to perform badly

MSFT Trading Strategy (Daily) update Oct 19th 2016

MSFT Trading Strategy (Daily) update Oct 19th 2016

 

WMT Daily. For a $10,000 investment, buy and hold returned $3,280. The trading strategy detailed here returned $13,632 over the period 1/8/13 to 2/11/15.

WMT Trading System (Daily)

For the period 1/8/13 to 2/11/15 the trading strategy returned $13,608 on an initial investment of $10,000. Buy and hold returned $3280. At the bottom of the graph are the signals. In this case the buy signals show good reinforcement, generally a buy signal is followed by several other buy signals. Sell signals show little reinforcement. Red and green bands show when the stocks were held long (green) or short (red).

WMT Daily. The tables show the annualized return of the trading strategy (50.8%) vs. buy and hold (14.7%) for the 2 year period 1/8/13 to 2/11/15. Drawdown was 6.2% vs. 10.5% for buy-hold. You can also see how you would have fared going only Long or Short or short-and-hold. The lower table describes the signals and the strategy in detail. The Long and Short strategy is described which requires holding the stock long or short at all times, however you would also have made a profit taking just the long or short side.

These graphs show how the return varies with changing the parameters of the algorithm. Note that for a sell point of 0%, all buy points to beyond 10% were profitable. However the algorithm was highly sensitive to changing the sell point, which had to be between 0% and 0.5% to give significantly advantageous returns. If outside of this narrow range, the algorithm was only as good as buy-hold.

The Life graphs show how the returns look over four different time periods. In general it is best to look for systems with consistent returns. For this strategy, all four time periods showed returns better than buy-hold.

WMD Daily. This shows how the return varied when the buy and sell points were varied. Notice the sell point range is much narrower that the buy point range. The algorithm only worked for a very small range of sell points.

Update Jan 9th 2016

WMT-D Table 1-9-16

Since original publication, the algorithm like the underlying stock has done poorly.


Update 10/19/2016

WMT Daily Trading Strategy Update 2016-10-19

WMT Daily Trading Strategy Update 2016-10-19