mholstius![]() Veteran ![]() ![]() ![]() ![]() Posts: 175 Joined: 1/13/2017 ![]() | 2018 was a particularly valuable year. Volatile, but valuable. The multiple reversals, swings, trend line breaks, consolidations, etc. – all condensed into 1 year – make it particularly valuable for testing and development. I regularly use RTM strategies. They have statistical confirmation over the long run and appear to be utilized by Institutional algorithms - a significant consideration when I started my Follow The Money portfolio in Elite back in 2017. Because I’ll be using that FTM portfolio for the examples in this thread, I feel it would be helpful to do a quick review of how it works and allocates equity to trades… FTM uses 5 dynamic lists to select stocks daily based on their money flow and correlation to the Dow, SP500, Small Caps, Financials, and Energy - hopefully avoiding some survivorship bias. The chosen symbols are then paired with various RTMs, creating 200 strategies. Those 200 strategies allow it to vary the allocation to any symbol based on how many times the symbol is traded by the various combinations of correlation and RTMs. It’s a process similar to multiple timeframe confirmation. If a symbol is only selected once on a day, it allocates the default allocation to that trade. If selected by more than one strategy (different RTMs and/or correlated with more than 1 index), it can allocate as much as 5x more to the trade. Remember, this was developed before we had the benefits afforded by ATM… I’ve been trading FTM for the past 2 years using a small default allocation of 2% / trade. If a symbol was chosen by 1 strategy it would allocate 2% to the trade. If chosen by 5 or more strategies, it would allocate up to 10% to the trade (2% managed by each of the strategies). I felt that the subsequent diversification would be a benefit. The results of this study indicate that I was wrong. A problem with RTMs is that they can have a string of small profits followed by a significant loss that can negate the gains. This post will examine a method that has the potential to reduce that problem. The most significant losses with RTMs come when a market downturn is substantial enough to trigger multiple RTM trades without making the rebound that they need to be successful… The RTMs are trying to catch the proverbial falling knife. I’ve expended a lot of effort attempting to develop a filter to stop RTMs from entering those trades but haven’t been successful – yet. Everything I’ve tested has a negative effect on the good trades that eliminates any benefit derived by avoiding trades during the DDs. (If you have any suggestions for filters you may have developed to avoid the DDs, I certainly invite you to share them... or at least hint at what you’ve found) Here’s a diagram to illustrate what I’m addressing concerning the balance between normal and DD allocations; What I’ve found is that when the “falling knife” situation occurs, RTMs usually enter enough trades to drive the allocation to 100%. Even if you can filter out some of them, there are often enough unfiltered trades available to still be 100% allocated in the wrong direction. That’s the crucial point. It’s difficult to stop the RTMs from allocating ~100% in “catch the falling knife” situations. They’re doing what they’re designed to do and entering trades in stocks that will, unfortunately, continue declining. The fact that they usually result in 100% allocation is useful information, since those catch the knife DDs can be treated as a somewhat stable problem – a loss often in the neighborhood of -10% at ~100% allocation. The “light going on” for me was the realization that we should be able to improve results by increasing the allocation during the periods that the RTMs are trading correctly – making the profits large enough to offset the falling knife losses. If thought of as CALMAR; trying to increase the Return side of the equation while the DD side is relatively fixed. Historically, I’ve followed rather typical trading rules and tried to mitigate risk by taking many small trades at a low allocation. Unfortunately, those “catch the knife” losses usually have allocations at a much higher level ( ~100%) that can negate the sum of my lower allocation wins. So, if the “catch the knife” DDs can’t be changed, what happens if we significantly increase the allocations to the normal trades - and thereby improve the gain side of the gain / loss ratio? To test the concept, I significantly increased my default allocation in my FTM portfolio for 2018. Instead of entering trades using from 2-10% / trade, I increased the multiplier so that it used from 20-100% / trade. The snag below shows 3 charts from 1/2018 to the present: SPY FTM, 2X margin, Long Only using the original 2-10% allocation FTM, 2X margin, Long Only using the modified 20-100% allocation The 6 “catch the falling knife” DD periods in 2018 are numbered in all three charts; Both FTM charts are approximately 100% allocated during the 6 falling knife periods and suffer approximately equal DDs during those periods. The critical difference is that the Modified chart has substantially better returns during the highlighted periods between the catch the knife DDs due to its higher allocations (visible in the allocation pane). Significant improvements from the Original to the Modified higher allocations; HR from 60% to 69% TPM from 104 down to 18 CAR from -12% to +60% Equity from $86,858 to $165,599 Trading with high allocations is not what I’m used to doing. It was something I probably examined 2 years ago and dismissed as a curve fit. Unfortunately for those of us trading FTM, it appears that I was wrong. We would’ve been much better off using these higher allocations in 2018. (sorry, all… maybe some of you did it on your own?) I haven’t changed any of the systems used by the portfolio for these tests. Any changes to the results in 2018 are due to the change in allocation. Since these new figures are so much better, I was concerned that it was just a curve fit to the unique situation in 2018. I found that the “out of sample” results for the previous 5, 10, and 15 years also showed substantial improvement and similar stability, as shown below; Avg Ann DD increases by ~6% Avg % Invested doubles (~90% vs ~45%) ~80% fewer # trades / month (15-17/mo vs 80-90/mo) CAR approximately doubled with substantially higher ending equities As I said, trading with these higher allocations will be new for me. I may find it a bit uncomfortable at first, but all the data indicates that it could be a significant improvement. As a result, I’ve increased the allocation levels in my FTM accounts and plan to just let it run. There will undoubtedly be times when a “normal” trade (not in a falling knife DD) will be negative - but this historical data indicates that the results should be stable, even while allowing for a slightly higher Avg DD. Higher allocations appear to be a better choice when trading RTMs (at least with FTM). They appear to counterbalance the ~100% allocation during falling knife DDs, and the 5, 10, and 15-year charts show a much more stable continuation of the curve into 2018, even with its volatility. I’ve been out of town taking care of some medical issues and haven’t had time to test this further. Given the superior ability of ATM to rank and filter trades, I expect to obtain even better results when using it. Add to that the possibility of raising the HR with AI, and this higher RTM allocation concept definitely merits more attention and testing. Since I feel that the concept is a significant finding, I wanted to share the information in a timely manner. I look forward to any comments and observation. Maybe some of you had already discovered this, already been trading with higher allocations using RTMs, and could provide more information or data…??? Mark [Edited by mholstius on 1/29/2019 5:22 PM] ![]() ![]() ![]() ![]() ![]() |