People trade all the time without giving a moment’s thought to the stats that exist all around them. However, machines are programmed to rely at least partially on statistics. The father of algorithmic trading, billionaire Jim Simons famously said “we search through historical data looking for anomalous patterns that we would not expect to occur at random.” In other words, his firm was digging through what happened with price in the past to predict with a statistical edge based on that historical data if price is going to move higher or lower.
Let’s take a look at what happened on Friday, August 13th, during the closing hour. As we can see below, price moved higher on the Spooz (E-mini S&P 500 futures) during the closing hour. Note that RSI 70 sellers made several fruitless attempts before the closing hour to sell when RSI got a bit overbought. Moreover, you’ll note the VWAP traders were watching price above Upperband 2, or 2 SD over the volume weighted average price, and they sold several times ahead of the closing hour, yet price kept moving higher. So, could we have used historical price action statistics to possibly predict this move? Answer: Yes! Incidentally, we could use Price Action Harvester™ to study historical price action stats for any timeframe, whether it’s 15 minutes, 40 minutes, 1 hour or 6 hours:
Let’s take a look under the hood of the chart. Let’s study historical price action, as we observe the closing hour of the S&P 500 for the last 12 Fridays. If we can determine either a bullish or bearish trend based on historical price action, we may be able to give ourselves a statistical edge in potentially predicting the direction price may move. For the purpose of short-term day trades, I don’t assign weight to the any of the stocks in the $SPX. Now, some may say what about weighting? Should we value Apple ($AAPL) as having the same same weight as Paycom ($PAYC)? Well, for our objective of predicting what may happen in this closing hour when compared to the last 12 Fridays, yes, we ignore weighting stocks. But why, the S&P 500 is weighted! That’s a valid argument, but when you’re developing a short-term day trading algo, you have to think the way the other programmers are likely to think, especially the programmers that work for the bigger market participants, as they tend to move markets.
As an algo developer, my assumption is that all the other algo developers that work for the larger market participants are going to do what is not expected of them. In essence, they take a look at the “happy path” and do the exact opposite. Here’s what I mean; this is a listing of the weighting of every stock in the S&P 500. This information is freely available to the public: https://www.slickcharts.com/sp500.
The typical algo developer will look at the above noted weighting, and will write their algo around it. Makes sense, because after all the S&P 500 is weighted. However, if day trading were a boxing match, writing your algo around a published set of data, is like throwing a 1-2 combination against Mike Tyson in his heyday. Mike is going to move under your jab (your 1 in the 1-2), right cross your rib, and uppercut your jaw; bam – lights out! In other words, never do the obvious; try not to program for the “happy path.” That’s how I approach my development of day trading systems; I assume the bigger players are going to do the opposite of what is expected. So I take the S&P 500 at face value – 500 stocks, all equally weighted. Now, if we were swing trading, and not day trading, then my algo would be completely different. However, we are day-trading, and we’re talking about the closing hour. The most we’re going to be in this trade is 60 minutes.
So I ran all 500 stocks of the S&P 500 on Price Action Harvester™, which took just a couple of minutes to run on NinjaTrader’s Market Analyzer, and the results were that 276 of the 500 stocks had moved higher at least 50% of the time when observed for the closing hour of the last 12 Fridays. Armed with this information, a day trader has essentially found a bullish statistical edge based on past price action; the only thing left to do now is to confirm if current price action (what’s happening right now on the chart) is also moving in the expected direction. Once you have that confirmation, then you’ve done all the statics based technical homework you can to place your short-term bet. This is how you get a statistical advantage based on past price action. This is how one may predict short term price movements. If you’re not a believer in statistics, and you tend to stick with moving averages, VWAP, RSI, or market profile etc., just remember nearly all other day traders have those indicators too. What’s more, these indicators are freely available with most trading software platforms, so you don’t really have any advantage over your competition, the other day traders who are taking the opposite side of your trade.
Check out a quick video I took of the 276 stocks from the S&P 500 that were up at least 50% of the time when observed over the closing hour of the last 12 Fridays. You can also take care of all the filtering requirements directly in Price Action Harvester™.
If you’re a day trader, you always want to give yourself a statistical edge based on historical or past price action. I developed Price Action Harvester™ for NinjaTrader 8, which is a statistics based indicator, as well as Price Action Harvester™ Automated, which is a fully automated trading system that relies on past price action, meaning what happened during particular days at particular times, as well as current price action. If you’re a serious short-term trader, I firmly believe you’ll be using these tools for the rest of your trading career. If you’re serious about trading, and have excess funds, I strongly recommend you invest in these advanced yet easy to use trading tools.
Remember, day trading should be a small subset of one’s overall investment portfolio; it’s just a more aggressive way of attempting to seek alpha when roaring inflation is eating away the purchasing power of the cash sitting in our savings accounts. Put the money to work.
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