[β¦]harder to find and smaller when I do find them. The only thing I can control is continuing to research for new strategies. I trade a variety of strategies based on mean reversion, trend following, breakouts and volatility. I have three mean reversion strategies in my trading stable and therefore care very much about how mean reversion is doing. The Idea Using SP-500 index data back to inception in 1957, I wanted to compare how the index did 1 & 5 days after either being in an over-sold or over-bought condition. Why back to 1957? I was curious to see [β¦]
[β¦]divided by 500 but the number of stocks in the index can vary around this number and since I have data to calculate the actual number, that is what I used. Then we apply a 40 day Moving Average to the indicator. The market is good when the MA40 of Hi-Lo Index becomes greater than 5%. When it is below 5%, we will avoid taking new positions. As a comparison, I will be testing using a simple Moving Average on the SPX as a market timing indicator. Baseline Rules Date range: 1/1/2005 to 9/30/2015 Setup Stock is member of S&P500 [β¦]
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In my previous post, Market Timing with a Canary, Gold, Copper, LQD, IEF and much more, I tested several market timing methods. The signal was checked on the last day of the month. Now the question is what happens if we check on a different day? How different will the results be? The Test The backtest is from 1/1/2004 to 12/31/2018 on the SPY, dividends included. Buy Rule On N days before the end of the month. Market timing gives a buy signal Enter on next Open Sell Rule On N days before the end of the month. Market timing [β¦]
[β¦]are trading now, they typically enter at the next dayβs open or wait for a further pullback intraday before entering. My current mean reversion strategy, which enters on a limit down, was doing great until a few months ago when the performance started to slip. Looking at the missed trades and the trades taken, it seemed like the best trades would have been entering at open or waiting for some intraday confirmation. Waiting for a pullback to take out the previous dayβs high was very popular when I started trading. But my testing then showed that it was better to [β¦]
[β¦]to simply sort by one my metrics and pick the first variation that meets my criteria. For novice researchers, this is what they gravitate to. The numbers great. A CAR way above my threshold and a MDD way below it. What could be wrong with this? Variations near the top are likely there because of luck. Small changes in the parameters may have large change in the results. The next post will be on this topic. Method 2: Avoiding top and low Ulcer Index preference To avoid picking a variation near the top, I do the following. First I sort [β¦]
[β¦]who ran the code. After comparing trade lists and doing some debugging, we discovered that their database was missing several symbols. These symbols existed in my database but did not exist in theirs. We were both using Norgate Data, which I highly recommend and can read my review here. They contacted Norgate and they walk them through fixing their database. This worried me. My theory was user error caused the problem. But more importantly, this could easily have been my database missing the symbols. Had we not discovered this discrepancy, this issue would still be hidden away. Before you think, [β¦]
[β¦]stocks out-performed the SPX with the weakest giving the best results. Will that be the case again? Test Parameters From 1/1/2001 to 10/31/2013 $.01/share for commission 3 month T-bill interest rate used for cash Β Best N Month Rules It is the first trading day of the month Stock is a member of the S&P500 Rank stocks from the highest to lowest by (6,12) month return Buy the top 20 ranked stocks at the close Optional market timing rule: SPX close > 200 day moving average of SPX closes on the entry day Hold until next month Β Worst N [β¦]
[β¦]have looked at low volatility stocks vs. high volatility stocks with trailing stops. Overall, the data pointed to trading lower volatility stocks. In this post, the focus is on low volatility stocks but now adding profit target stops to see how they can improve the results. Trailing Stops The following profit targets will be tested. (10, 20)% percent stop (2,4)x ATR10 Stop (2,6) Month Stop Profit Targets The following profit targets will be tested. None (20,40)% target (2,4,6)x ATR10 target Β The Test Date range from 1/1/2004 to 12/31/2013. Buy Rules Stock has 21 day moving average dollar-volume greater than [β¦]
[β¦]returns much better than I would expect. This observation was recently echoed by another researcher that I know. Could one make a market beating system by basically randomly pick stocks? The research that led to this observation was on market timing. Can having a good market timing rule, a profit target and stop loss be enough to randomly pick stocks and beat the market. The answer may surprise you. Testing Parameters Test date range is from 1/1/2004 to 12/31/2013. Entry an exit are done at the close. Maximum number of open positions is 10. Market Timing Rules Tested Close is [β¦]
