Before my current circumstances, and before I was a photographer (see above), I used to make music for a living. Specifically, weird-ass techno/electronic music that many people found difficult or annoying. One of the ways I would find sonic inspiration was to use audio software to generate random sounds. I would record this stream of noisy squawkiness, sift through a lot of garbage, and occasionally find a useful gem. I would take these little bits of useful audio and turn them into gritty, weird dance music.
It’s possible to find dedicated software that dives deeply into finding non-obvious, non-linear connections between “features” of price data. For example, we can ask ourselves if today’s high of the price of oil is above its three-day moving average, and the S&P 500’s closing price is below yesterday’s open, will gold go up the next day? Continue reading Randomly Pushing Buttons
I was mulling over the question of what happens when the market opens up, i.e. above its previous close. Is the day likely to be an up day? A down day? I got out my data and started poking around. I looked at all “open-up” days with an open at least 0.25% above the previous day’s close. I looked at only days that opened up after a previous close-to-close down day. And the reverse.
The statistics were not significant, although it appeared there was something of a shorting opportunity there. I therefore put together a backtest for shorting at the open and holding to the close, and that looked like utter garbage. Continue reading Open Up!
There are two systems I’ve been trading since the beginning of 2016. That’s fifteen months of true out-of-sample, real live experience. I’m pleased to report the results have been very good, and consistent with their original backtests.
When stocks are moving gently from one day to the next, there is often no discernible pattern. However when they start rockin’ and rollin’ one direction or the other, they show certain similarities.
I’m always curious how stocks behave when they show a significant drop, or when they pop upward unexpectedly. I ran some simple statistics and noticed a couple of things.
First, I decided to look at what happens when stocks drop significantly. Rather than look at a fixed percentage, I instead used Average True Range of the stock. This shows the average price movement over the previous days, and is a measure of volatility. I took the ATR(20) before a drop, and corralled all the stocks that fell at least 3x the previous day’s ATR(20) value. I also looked at stocks that dropped at least 5x the previous ATR. Here’s a visual:
I then recorded the percent gain or loss for each day’s close following the drop, for five days after. This resulted in thousands of rows of data, but you know I gladly suffer through spreadsheet hell so that you can have pretty graphs.
Yesterday I discussed two swing-trade systems that work pretty well in out-of-sample data. While each works differently, they overlap enough that you don’t get any benefit from running them both at the same time. One great thing about these two systems is that they’re dead simple to manage. Trade at the open or the close, simple math, etc etc.
I will repeat the caveat from yesterday: these trades average <1% gain per trade. You must have sufficient capital and/or a low/nonexistent commission fee to make these work. While you can use leveraged ETFs or account leverage to help increase the profit/commission ratio, you also increase your chance of a catastrophic hole in your money.
In the lead image, you can see that I have indicators for both RSI and PIRDPO. PIRDPO occurs more frequently, and the RSI trades are a complete subset of the PIRDPO trades (during this particular time frame). There is no benefit to trading both systems.