Bull And Bear Regime Trading - How To Algo Trade With Trends Without Getting Run Over By Them!

• Bull Market
• Bear Market
• Flat Market

• Volatile Market
• Non-Volatile Market

With that in mind, let’s see if a market regime project yields any useful results in this four part article.

Section 1- Bull, Bear and Flat Market Regime Trading

Section 2 — Bull and Bear, With Enhanced Flat Market Regime Trading

Section 3 — Volatile and Non-Volatile Regime Trading

Section 4 — Bull, Bear, Flat, Volatile and Non-Volatile Regimes, All 6 Combinations

Section 1 — Bull, Bear and Flat Market Regime Trading

Core Strategies For This Study

Strategy #1 is a simple breakout strategy. If the current close is the highest close of the last X bars, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #2 is a reverse simple breakout strategy. If the current close is the highest close of the last X bars, it sell short at the open of the next bar with a market order. Vice versa for long trades.

Strategy #3 is an RSI strategy. If the RSi is less than 30 (oversold), it buys at the open of the next bar with a market order. Short trades are signaled for RSI values above 70.

Strategy #4 is a reverse RSI strategy. If the RSi is less than 30 (oversold), it sells short at the open of the next bar with a market order. Long trades are signaled for RSI values above 70. Sample code for Tradestation is shown below.

Strategy #5 is a simple momentum strategy. If the current close is greater than the close X bars ago, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #6 is a reverse simple momentum strategy. If the current close is less than the close X bars ago, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #7 is a traditional moving average strategy. If the current close crosses above the average close of the last X bars, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #8 is a reverse traditional moving average strategy. If the current close crosses below the average close of the last X bars, it buys at the open of the next bar with a market order. Vice versa for short trades.

Note that each of these core strategies is a pretty common approach to trading. I chose them because they were simple and fairly well known.
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Each of these strategies has a single parameter than can be optimized — the lookback length X. I will use 10, 30 or 50 bars for this optimization.

Bull/Bear/Flat Filters For This Study

Filter #1 — “Always” Trade Filter. Allow long and short trades with no restrictions.

Filter #2 — Moving Average Filter. Only allow long trades when the current close is above the average close over the last Y bars. Vice versa for short trades. This is a bull or bear filter. Tradestation code is shown below as an example.

Filter #3 — Momentum Filter. Only allow long trades when the current close is above close Y bars ago. Vice versa for short trades. This is a bull or bear filter.

Filter #4 — High Or Low First Filter. Over the last Y bars, did the highest high occur more recently than the lowest low? If so, take only long trades. Vice versa for short trades. This is a bull or bear filter.

Filter #5 — ADX Filter. Only allow trades when the Y bar ADX is above 15. This value suggests at least a weak trend is in place. Below 15 for the ADX, just remain flat. This is a flat or not flat filter.

Filter #6 — RSI Filter. If the RSI over the last Y bars is below 70, only allow long trades. If the RSI over the last Y bars is above 30, only allow short trades. This is a bull or bear filter.

As you can see, most of the filters break down the market action into a bull market or a bear market. In part 2, I’ll take one of these filters and add specific flat regimes to it.

Markets and Bar Sizes For This Study

Daily, 720 minute, 360 minute, 120 minute and 60 minute bars

Other Study Info

• 40 markets
• 5 bar sizes
• 8 strategies
• 3 values for length X for each strategy
• 6 bull/bear filters
• 3 values for length Y for each filter

This will produce 86,400 unique iterations — WOW!

You might say — this will take a long time to complete, as it is a big study with many markets and bar sizes. How can you do this in a timely fashion?
Well, to help me out, I will be using Multi-Opt, a special software tool created specifically for Tradestation. This software allows for rapid testing and prototyping of strategies — just one of many timesaving tasks it performs.

Results

Overall Net Profit
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Maximum Intraday Drawdown

I obviously could have chosen a ton of various metrics, but one metric for profit and one for risk should tell me what I want to know.

I should also point out that different metrics might lead to different conclusions. Bottom line, if you are analyzing the markets in a scientific fashion, you should pick performance criteria you feel is appropriate. You might pick wildly different metrics than I did, and that is OK.

With that, let’s get started with analyzing the results.

First, let’s take a look at the impact of the Regime Filter:

But filters #2 thru #5 show a pretty dramatic improvement in average Net Profit compared to the baseline, and also a significant reduction in maximum drawdown. This shows that the filters appear to work!

Filter #6 shows basically no change from the baseline. I am guessing it is not doing much if any filtering at all.

Maybe though, the averages are deceiving, and possibly skewed by a few strategies. Let’s take a look at the results for each strategy:

​The results are clear — filters #2 — #5 all improve Net Profit and Max Drawdown, for each of the 8 strategies. Sometimes the impact is small (for example with Strategy #4), and other times it is dramatic (for example with strategy #8).

What is surprising is that it does not matter much which filter is used — their results are all more or less the same. Maybe these particular filters all catch the same big trends?
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What about the lookback length of the filter, which in the test was set from 100–200 bars? Does this variable play a role?

Again, a lot of data in the table below — just focus on the green highlighted cells.

What Conclusions Can We Draw So Far?

With so many markets, bar sizes, etc. there are many ways to dissect this data. But for this study I’d like to keep it general.

Trend Based Bull/Bear Regime Filters On Average Improve Net Profit, And Reduce Drawdown

For Filters That Work, There Is Not A Large Degree Of Variation Between Best Filters and Worst Filters

The Longer The Regime Filter Lookback Length, The Better The Results

Variables: CanTradeLong(False), CanTradeShort(False);
CanTradeLong = False;
CanTradeShort = False;
If close> close [200] Then CanTradeLong = True;
If close <close [200] Then CanTradeShort = True;

if CanTradeLong = True and {my long entry rule} then buy next bar at market;
if CanTradeShort = True and {my short entry rule} then sellshort next bar at market;

A Small Inspiration For You

Quite a difference!

After doing this initial study, a few lingering questions still bothered me a bit.

What is the impact of instead of just bull/bear regimes, adding in a flat regime, to make it bull/bear/flat?
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Filter #6 (RSI filter) did nothing — why?

Those questions will be discussed below.

Section 2 — Expanded Bull, Bear and Flat Market Regime Trading

So first I will tackle the second topic. In Part 1, I had filter #6 — a simple RSI filter. It did pretty much nothing — why?
Here is its definition:

Filter #6 — RSI Filter. If the RSI over the last Y bars is below 70, only allow long trades. If the RSI over the last Y bars is above 30, only allow short trades. This is a bull or bear filter.

When I added this, I really did not look too much into it, I just used fairly loose overbought and oversold levels of 30 and 70, figuring it would work.
Not so fast my friend!
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Turns out when you calculate RSI over 100–200 bars, the value rarely, if ever, gets above 70 or below 30. Here is an example:

For me, trying too hard usually leads to curvefitting and overfitting.

As a result, I am going to eliminate RSI from trend filtering. I could probably play around with it, and get it to work, but I am always wary of trying to make something work. For me, trying too hard usually leads to curvefitting and overfitting.

In Part 1, the moving average Filter (#2), the momentum Filter (#3), the High Or Low First Filter (#4) and the ADX Filter (#5) all did reasonably well, with the ADX filter being the best.
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For this part of the study, I am going to use the momentum filter #3, which was in the middle of the pack on filter performance.

Mome Filter #1 — “Always” Trade Filter. Although long and short trades with no restrictions. This should equal (or be very close to) Filter #1 in Part 1.

Mome Filter #2 — Momentum Filter. Only allow long trades when the current close is above close Y bars ago. Vice versa for short trades. This is a bull or bear filter. This should equal (or be very close to) Filter #3 in Part 1.

Mome Filter #3 — Momentum Filter. Only allow long trades when the current close is above close Y bars ago AND if the current close is above the close Y/2 bars ago. Vice versa for short trades. This filter will be flat whenever there is conflicting short and long period momentum.

Mome Filter #4 — ADX Filter. Only allow long trades when the current close is above close Y bars ago AND if the ADX is above 15 (signifying trend mode). Vice versa for short trades. This filter will be flat when the ADX is flat.

Momentum 3 and 4 filters (with “flat” periods as opposed to only bull and bear periods) do perform better. Both the net profit and max drawdown improved by about 60%. That is pretty good performance improvement.
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When the results are examined across all 8 of the core strategies, the filters improve each strategy. Sometimes the difference is small, and sometimes it is very large.

Digging a little deeper, the impact of the various filters can best be seen with the average number of trades. Compared to no filter (Mome Filter #1), many of the trades get filtered out with Mome Filter #4. An example of that is shown below:

With no trades filtered out, this strategy has 1153 trades. Quite a few!

The smaller the number of trades, the less reliable the results will be.

Overall Results

• Bull market — can only initiate long trades, simultaneously exiting the short trade
• Bear market — can only initiate short trades, simultaneously exiting the long trade
• Flat market — cannot exit or enter trades during the flat period
  
  This approach does not keep from being in trades during flat periods; it just restricts new trades during flat periods. That is sort of “half flat!”
  As a result, I decided to run the cases again, adding this code for each strategy:

New Results — Bull/Bear/Flat Filter vs. Bull/Bear Filter

​NEW FILTER

ORIGINAL FILTER

Conclusion of Section 2

But they have a low number of trades, so they are considered suspect.

So, that leaves us with this:

Big Takeaways From Study, To This Point

Bull/Bear/Flat Filters Work Better Than Having No Filters

With Varying Results Across Filters, It Is Good To Verify Filters For Your Particular Strategy

Section 3 — Volume and Volatility Filters

We are testing with 5 bar sizes (Daily, 720 minute, 360 minute , 120 minute, 60 minute)

@AD, @BO, @BP, @C, @CC, @CD, @CL, @CT, @DX, @EC, @EMD, @ES, @FC, @FV, @GC, @HG, @HO , @JY, @KC, @KW, @LC, @LH, @NG, @NK, @NQ, @O, @OJ, @PL, @RB, @RR, @S, @SB, @SF, @SI, @SM, @RTY, @TY, @US, @W, @YM

Strategy #1 is a simple breakout strategy. If the current close is the highest close of the last X bars, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #2 is a reverse simple breakout strategy. If the current close is the highest close of the last X bars, it will sell short at the open of the next bar with a market order. Vice versa for long trades.

Strategy #3 is an RSI strategy. If the RSi is less than 30 (oversold), it buys at the open of the next bar with a market order. Short trades are signaled for RSI values above 70.

Strategy #4 is a reverse RSI strategy. If the RSi is less than 30 (oversold), it sells short at the open of the next bar with a market order. Long trades are signaled for RSI values above 70. Sample code for Tradestation is shown below.​

Strategy #5 is a simple momentum strategy. If the current close is greater than the close X bars ago, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #6 is a reverse simple momentum strategy. If the current close is less than the close X bars ago, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #7 is a traditional moving average strategy. If the current close crosses above the average close of the last X bars, it buys at the open of the next bar with a market order. Vice versa for short trades.

Strategy #8 is a reverse traditional moving average strategy. If the current close crosses below the average close of the last X bars, it buys at the open of the next bar with a market order. Vice versa for short trades.

Test and verify on your own — that is my mantra!

VolVol Filter #1

VolVol Filter #2

Allow trades only if 1 bar ATR (Average True Range) is ABOVE average ATR over the last 100,150 or 200 bars

VolVol Filter #3

Allow trades only if 1 bar ATR (Average True Range) is BELOW average ATR over the last 100,150 or 200 bars

VolVol Filter #4

Allow trades only if current bar volume is ABOVE average volume over the last 100,150 or 200 bars

VolVol Filter #5

Allow trades only if current bar volume is BELOW average volume over the last 100,150 or 200 bars

VolVol Filter #6

Allow trades only if 1 bar ATR (Average True Range) is ABOVE average ATR over the last 100,150 or 200 bars AND if current bar volume is ABOVE average volume over the last 100,150 or 200 bars

VolVol Filter #7

Allow trades only if 1 bar ATR (Average True Range) is ABOVE average ATR over the last 100,150 or 200 bars AND if current bar volume is BELOW average volume over the last 100,150 or 200 bars

VolVol Filter #8

Allow trades only if 1 bar ATR (Average True Range) is BELOW average ATR over the last 100,150 or 200 bars AND if current bar volume is ABOVE average volume over the last 100,150 or 200 bars

VolVol Filter #9

Allow trades only if 1 bar ATR (Average True Range) is BELOW average ATR over the last 100,150 or 200 bars AND if current bar volume is BELOW average volume over the last 100,150 or 200 bars

Results

For filters 2 and 3, the average true range filters, Net Profit is worse, and the Max Drawdown is not much changed.

For filters 4 and 5, the volume filters, Net Profit is worse, but the Max Drawdown is somewhat less.

For all 4 of those, there is not enough evidence to say “we’ve found the Holy Grail!” :( :(

For the combination filters 6–9, filters 7 & 8 both have improved Net Profit and reduced Maximum Drawdown. These cases also have a lot less trades when compared to the baseline case. This suggests that the filters are doing their job, and reducing the number of bad trades.

Filter 7 like above average volatility, and lower than normal volume.
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Filter 8 likes below average volatility, and higher than normal volume.

Here are sample results, with filter #7 and filter #8:

Of course, there are cases where the filters make things worse, but based on the overall numbers, MOST times using filters #7 or #8 should improve performance.

What I’d Be Testing On My Possible Strategies, Based On Section 3 Results

VolVol Filter #7

Allow trades only if 1 bar ATR (Average True Range) is ABOVE average ATR over the last 100,150 or 200 bars AND if current bar volume is BELOW average volume over the last 100,150 or 200 bars

VolVol Filter #8

Allow trades only if 1 bar ATR (Average True Range) is BELOW average ATR over the last 100,150 or 200 bars AND if current bar volume is ABOVE average volume over the last 100,150 or 200 bars

Section 4 — Putting It All Together

• Filter # 1 — No filter
• Filter #2 — Part 2 — Simple Momentum Bull/Bear Filter
• Filter #3 — Part 2 — Simple Momentum Bull/Bear Filter, with Part 3 VolVol filter #7
• Filter #4 — Part 2 — Simple Momentum Bull/Bear Filter, with Part 3 VolVol filter #8
• Filter #5 — Last Section of Part 2 — Simple Momentum Bull/Bear/Flat Filter
• Filter #6 — Last Section of Part 2 — Simple Momentum Bull/Bear/Flat Filter, with Part 3 VolVol filter #7
• Filter #7 — Last Section of Part 2 — Simple Momentum Bull/Bear/Flat Filter, with Part 3 VolVol filter #8

Results

Bull/Bear/Flat filter is better than plain Bull/Bear filter (for drawdown, not profit — compare filters #2 and #5)

VolVol filter #8 is better than VolVol #7, when combined with Bull/Bear Filter

Filter #7 (Bull/Bear/Flat filter with Volvol #8 filter is best overall)

“Sure, Net Profit improves, and Max Drawdown is lower, but how do you know that this is not just due to filtering? In other words, maybe the filters are just getting rid of a lot of trades — good and bad — and not necessarily just the bad ones!”

That is the problem with big datasets and lots of performance numbers. You can easily draw different conclusions based on which data you look at!
So, let’s look at a few different numbers, one familiar, one not so familiar.

Average Net Profit is a great way to see if the filters are filtering out primarily bad trades, or if they are just filtering out all trades:

• Maximizing Net Profit
• Minimizing Max Drawdown
• Maximizing Average Profit Per Trade
• Maximizing Good results / Bad Results

​What is best for me might not be best for you!

In any event, here is a sample of the progress we’ve made in this study.

First, we start out with a simple strategy (strategy #5 for this example). It does not look too great…

CanTradeLong=True; //no filter
CanTradeShort=True; //no filter
if CanTradeLong = True and close> close [inputvar2] then buy next bar at market;
if CanTradeShort = True and close <close [inputvar2] then sellshort next bar at market;

CanTradeLong = False;
CanTradeShort = False;
If close> close [InputVar4] Then CanTradeLong = True;
If close <close [InputVar4] Then CanTradeShort = True;
if CanTradeLong = True and close> close [inputvar2] then buy next bar at market;
if CanTradeShort = True and close <close [inputvar2] then sellshort next bar at market;

The smaller the number of trades, the less reliable the results will be.

// momentum with volvol filter 8
CanTradeLong = False;
CanTradeShort = False;
If close> close [InputVar4] and Volume>averagefc(volume,InputVar4) and AvgTrueRange(1)<AvgTrueRange(InputVar4) Then CanTradeLong = True;
If close <close [InputVar4] and Volume>averagefc(volume,InputVar4) and AvgTrueRange(1)<AvgTrueRange(InputVar4)Then CanTradeShort = True;
if CanTradeLong = True and close> close [inputvar2] then buy next bar at market;
if CanTradeShort = True and close <close [inputvar2] then sellshort next bar at market;

// momentum with flat period and volvol filter 8
CanTradeLong = False;
CanTradeShort = False;
If close> close [InputVar4] and close> close [InputVar4/2] and Volume>averagefc(volume,InputVar4) and AvgTrueRange(1)<AvgTrueRange(InputVar4) Then CanTradeLong = True;
If close <close [InputVar4] and close <close [InputVar4/2] and Volume>averagefc(volume,InputVar4) and AvgTrueRange(1)<AvgTrueRange(InputVar4) Then CanTradeShort = True;
if CanTradeLong = True and close> close [inputvar2] then buy next bar at market;
if CanTradeShort = True and close <close [inputvar2] then sellshort next bar at market;

The best part of this transformation is that it is not just for specific cases. This improvement seems to hold up over many strategies, markets and timeframes.

Now realize this is just an example — you MUST test your particular situation. Maybe it will help, but there is a chance it will not help.

What I’d Be Testing, Based On This Study — Option 1

// momentum with flat period and volvol filter 8
CanTradeLong = False;
CanTradeShort = False;
//Set InputVar4 to between 100 and 200 (not much difference from 100 to 200)
If close> close [InputVar4] and close> close [InputVar4/2] and Volume>averagefc(volume,InputVar4) and AvgTrueRange(1)<AvgTrueRange(InputVar4) Then CanTradeLong = True;
If close <close [InputVar4] and close <close [InputVar4/2] and Volume>averagefc(volume,InputVar4) and AvgTrueRange(1)<AvgTrueRange(InputVar4) Then CanTradeShort = True;
if CanTradeLong = True and YOUR LONG ENTRY then buy next bar at market;
if CanTradeShort = True and YOUR SHORT ENTRY then sellshort next bar at market;

What I’d Be Testing, Based On This Study — Option 2

// momentum buy/bear filter only
CanTradeLong = False;
CanTradeShort = False;
//Set InputVar4 to between 100 and 200 (not much difference from 100 to 200)
If close> close [InputVar4] Then CanTradeLong = True;
If close <close [InputVar4] Then CanTradeShort = True;
if CanTradeLong = True and YOUR LONG ENTRY then buy next bar at market;
if CanTradeShort = True and YOUR SHORT ENTRY then sellshort next bar at market;

I think I have found some very interesting things, which I plan to use in my future strategy building (and maybe you should too):

Taking only long trades in a bull trend (and short trades in a bear trend) is better than allowing all trades anytime

Having defined flat periods can be a good thing

Adding filters for higher than average volume, and lower than average volatility (Average True Range) seem to be best

Results can vary, that is why incorporating regime filters should only be done as a part of a solid strategy development process (in other words, if you over-optimize these filters, you will be in WORSE shape!)

Previously published at