Using Python in Build Alpha [Creating Custom Signals]

As 2017 comes to an end, I want to introduce one more upgrade to Build Alpha in its first year. As you know Build Alpha allows users to create, stress test, and even generate tradable code without ANY programming at all.

It also allows traders to use a custom drag and drop signal builder to create unique rules and signals to test alongside the pre-built Build Alpha signal library of nearly 5,000 signals – and growing (below is a picture of the drag and drop custom signal builder).

Trading Strategy

However, I have now added a python environment to give traders even more freedom. Traders now have the ability to code their own signals in python and test these signals in the Build Alpha strategy creation engine.

Again, you do NOT need ANY programming skills to use Build Alpha but if you WANT to you can now use python to create signals, but you do NOT have to as Build Alpha will work without. This is just an upgrade for the more sophisticated traders out there.

Below is a simple, 5 step walk through of how to create a simple signal using python and test it with other pre-built signals in Build Alpha.

o_hl

Build Alpha produces a simple to use template. All we need to change is the variable file_name_base. Set this variable equal to the file you wish to do analysis on. I have used the built in Build Alpha data in the example below.

*Note* You can find the file path by right clicking on your data file, selecting properties, and copy/pasting the path inside two quotes.

filepath
signalcalc

This is a simple and introductory example. I will create another post using some more advanced machine learning algorithms later on.

This opens the door for what is possible in Build Alpha now. Traders can leverage the power of python as well as Build Alpha in extremely easy to use ways.

It has been one heck of a year for Build Alpha’s development and there is still so much to do in 2018! Thanks for all those that support the software; I am looking forward to next year.

Thanks,

Dave

Thanks for reading,
Dave

Mean Variance Optimization [Portfolio Construction]

 

Here is the definition from Investopedia.com: “Mean – Variance analysis is the process of weighting risk (variance) against expected return. By looking at the expected return and variance of an asset, investors attempt to make more efficient investment choices – seeking the lowest variance for a given expected return or seeking the highest return for a given variance level.”

It also assumes investors are rational and would choose the asset that had lower variance if the expected return of two assets were equal. I will assume you all are!

In layman terms, this test shows two things. First, if adding a strategy increases the overall risk:reward of your portfolio or not. Second, what are the appropriate weights each strategy in my portfolio should be assigned? This is certainly a crude explanation of mean-variance optimization, but this isn’t an academic blog.

 


 

The Test

This test can be done with either historical or “predicted” returns. We will assign random weights to each of the strategies in the portfolio; the sum of all the weights shall equal 1 (or 100% of allocated capital). After assigning weights to each of the strategies we can generate a risk-adjusted return value or Sharpe Ratio. This is the Sharpe Ratio had we traded each of these strategies with the weight we just assigned to each.

After thousands of tests assigning random weights we generate a plot of the thousands of Sharpe Ratios. We are trying to find the best weightings to lower the variance in our portfolio while keeping the return as high as possible.  The graph’s legend or color bar shows the highest Sharpe Ratio possible. This portfolio contains the three “checked” strategies on the far left of the photo below and the highest Sharpe from this portfolio is 2.17.

 

mv_1

 

 

We can then hover over the graph and view the weights that produced a specific Sharpe value or dot on the chart.  The idea here is we can see how to weight the strategies in our portfolio to give us the “optimal” or best risk-adjusted reward – which is our ultimate goal as trading system developers.

The red dot shows equal weights or how our portfolio would have performed had we traded each strategy with the same position size.

Now to answer the question “does adding this strategy to my portfolio help or hurt expected performance?”.  In Build Alpha’s portfolio mode you can simply include or remove strategies and quickly see if the Sharpe Ratio increases or decreases. Below I have selected one additional strategy (see checks on far left of photo below) and you can see a Sharpe Ratio improvement from our original 2.17 to 2.50.

Adding new individual strategies will almost certainly “smooth” out the portfolio’s equity curve, but does it actually increase risk:reward? This test gives a simple, quick way to quantify the addition (or subtraction) of a strategy.

 

 

mv_2

 

 

Here I have changed the ES (S&P500 strategy) in the portfolio. I unselected the top ES strategy and selected the other ES strategy. You can see the Sharpe Ratio worsening as it appears this portfolio only achieved 2.28 (vs. previous portfolio configuration of 2.50). This is a good strategy, but just does not work as well with the other strategies in the portfolio as the original ES strategy we had included.

 

mv_3

 

 

We want our money to be as efficient as possible and this test gives us another check if it is or not.  No point in trading a strategy that is not expected to increase your overall risk reward.

It is now much simpler to compare strategies or to decide whether or not to include a strategy into a portfolio or not. Much of trading comes down to making informed decisions that maximize risk:reward and running this type of analysis certainly increases the information at your disposal.

 

-Dave

 

PS: Build Alpha allows traders to import systems built outside Build Alpha for analysis and portfolio construction. So you can do all of this analysis on Build Alpha strategies, your own strategies, or any combination between.

Thanks for reading,
Dave

Free Friday #20 – Time Windows

There has been a recent popularity regarding time windows and it is one I completely agree with! There are certain structural changes that happen throughout the 24 hour session and as a trader it is important to take note of these when designing a system or strategy (or just placing trades). For example, how is my strategy’s performance when Asia closes? How about when the US opens?

There are also some blatant market anomalies that still exist regarding time windows. The most obvious one, and often popularized by Eric Hunsader of Nanex (and others), is the S&P500 futures performance from 2am to 3am. This little one hour window looks like quite the edge – and has been persistent despite being publicly known for quite some time.

3am

There are plenty of ways to use the time of the day to your advantage. For instance, only find trades/patterns that exist during a window of time where you might be at your desk. Another simple approach is to filter out unfavorable periods of the day to improve your performance (obviously proper testing methods important here).

My main point is… if you start paying attention to time windows you may be surprised as to what simple edges you’ll discover. It has certainly been a staple in my research for quite a while and know many other successful traders who put a lot of emphasis on this.

I’ve recently upgraded Build Alpha to allow for intraday strategy creation and time window filtering (among other things). Inside BA you can now select (and create your own) time windows to use as input in the strategy creation engine. There is also a time of day test which allows you to select certain windows to see if it improves strategy performance.

Below is a simple strategy that shorts EURUSD (I used the futures contract) at 5:00 am EST every Wednesday, Thursday, and Friday morning. This idea simply holds the short trade for 3 hours.

eurusd_short_mc

These might not be standalone strategies, but can certainly give you a head start for continued research, act as a filter for other strategies or be paired with other ideas to create fully operational strategies.

Trading is extremely competitive. Looking at price alone is may be too naïve in today’s market; there is simply too much computing power looking at it. If you’re struggling to find quantified edges then maybe it is time (pun intended) to start pairing price action with time filters.

As always, thanks for reading. And please stay safe with all these hurricanes!

Dave

Thanks for reading,
Dave

Intraday Checks and Hidden Edges

 

What is an intraday check? It is a phrase I am trying to coin, but I will explain the concept here..

There are certain rules (mainly for intraday strategies) that I believe should be applied to each strategy to check if there are any “hidden edges” in the underlying strategy.

So what are these rules? And how can we test them? Well Build Alpha now offers a test that allows users to test any intraday strategy to find out if it is as efficient as it can be or if there are any “hidden edges” within the strategy as I like to call it. Here are the additional intraday rules or checks.

  1. Time windows. Is the strategy improved by only trading between certain times of the day?
  2. Maximum number of trades per day. Is the strategy improved by limiting the number of trades per day it makes?
  3. Minimum P&L per day. Is the strategy improved by limiting the amount of money it is allowed to lose per day. For example, automatically turn the strategy off for the day once you’ve lost $500 today.
  4. Maximum P&L per day. Is the strategy improved by stopping the trading when an amount of money is earned per day. For example, automatically stop trading for the day once you’ve made $2,500 today.

Obviously, these tests are common and obvious ideas to look for hidden edges and strategy improvements – and of course, with all things, we need to use proper data analysis techniques (in vs out of sample, sample size, etc.) which I am not going to get into in this post as I have other posts regarding that.

The Intraday Checks test can be easily configured by clicking on the “Test Settings” button and configuring the right hand side of the pop up window seen below:

 

intradaysettings

 

The coolest feature of this addition to the software is the ability to add any of the newly found rules to the generated code for any of the supported Build Alpha platforms. For example, if you find a rule (or few rules) that improves the original strategy then you can double-click the test result and Build Alpha will automatically incorporate those rules when generating trading code for TradeStation, MultiCharts, Ninjatrader or MetaTrader.

Here is a photo after selecting (double clicking) on a test result which would then add this test’s rules to the exportable code. The highlighted red (selected) strategy’s rules can be seen at the bottom of the graph.

 

intradaytestblog

 

 

This selected strategy would add the rules

-Max Trades per day: 2
-Only take entries between 2:00 am and 12:00pm
-Stop trading each day after losses reach $1,000 per day

Finally, the histograms below the graph shows the distribution of all the tests on the intraday rules. This is important to make sure you’re not just selecting an outlier but that the underlying distribution/simulation itself was strong. Again, this post is not meant to get into selection bias and other potential pitfalls as I have other content about that.

All in all, it is important to search every nook and cranny to make sure we’re not leaving money on the table or missing an easy edge or strategy filter. This test aims to help in that regard.

Thanks for reading,
Dave

Improving Strategies

A crazy cool way to use Build Alpha. I have to admit that I did not come up with this idea, but it was suggested to me by a potential Build Alpha user.

He was wondering if Build Alpha could help come up with some rules of when he should avoid trading his existing strategy or even when to fade his existing strategy. Heck any improvement is a plus, right?

 

**Please note Build Alpha now accepts data in this format:  mm/dd/yyyy, hh:mm, open, high, low, close, volume, OI. Please refer to buildalpha.com/demo page for adding own data instructions**

*I say we found one strategy but we actually found tons that would be an improvement to his original strategy. Him and I only spoke specifically about one so that’s why in the video I slip and say we found one strategy. Did not feel like making a new video to clarify this minor point.*

He had a day trading system and compiled profit and loss results for that system in the following (Build Alpha accepted) format. Date, time, open, high, low, close, volume.  (*note Build Alpha now accepts the time column as intraday capabilities are becoming fully operational*).

Below is his sample file. We purposely left the open (high and low) columns as all 0’s. The close column contains the end of day p&l from his original strategy.

 

 

pnl

 

 

We then set Build Alpha to have a maximum one bar holding period and to ONLY enter on the next bar’s open and to ONLY exit on the next bar’s close. I will explain why this is in a minute.

 

We then chose the underlying symbol the original strategy was built on as market2. So for example, his original strategy trades ES (S&P500 eMini futures) so we only select Build Alpha signals calculated on Market2 which is set for ES.

So now if Build Alpha calculates a rule on ES like close[0] <= squareRoot(high[0] * low[0]) then we would “buy” the next bar’s open of market1 (again his results – which are 0) and “sell” the next bar’s close of his results which is the original strategy’s p&l for that day. This would essentially say that if this rule is true then go ahead with a green light to trade the original strategy the next day. If the rules are not true, then don’t trade the original strategy the next day. Ideally, we can find rules that increase risk-adjusted returns for the original strategy (which we did).

Now what is even cooler is if we set Build Alpha to find short strategies we would essentially be “fading” his original strategy or finding rules of when to go opposite his original strategy.

Build Alpha found some good short/ “fade” rules to use as well. Here is an example that did quite well fading his original strategy (even out of sample – highlighted section).

 

dtfadeexample

 

After emailing him the results here is what he had to say in his email response:

“There are 2028 negative periods in my data with a gross loss of -1,217,880.26. That’s the theoretical maximum a short rule can achieve, if it were to find all losses. Your graph seems to show 380,000 short rule profits. That’s already 31% of all losses. If I don’t trade on these days, my net profit would go up by 380,000, a 46% increase.”

 

I thought this was a really unique way to use Build Alpha and I wanted to share. I think the same analysis can be done on strategies with longer holding periods too. I would just import daily marked to market results of the original strategy and Build Alpha would essentially find rules of when to hedge your strategy or fade it for a day or two. I think this is certainly an unique approach to add some alpha to performance.

Anyways, thanks for reading as always and keep a look out for some MAJOR upgrades coming to Build Alpha very soon!

Thanks,

Dave

Thanks for reading,
Dave

$70,000 in profits in just 3 months. A Build Alpha testimonial

 

This is hands down the best email I have received since launching the Build Alpha software a little over six months ago. It is a thank you note sent from a Build Alpha user, Madhur, who licensed the software back in March 2017 and has grown his account about $70,000 in that span (or about +55.92%).

Below is a photo of the email he sent and his account statements verifying his amazing first three months of trading while using the software.

 

madhur_1

 
madhur_2

 
madhur_3

 

 

What is amazing is that Madhur is/was a discretionary or hand trader! That’s right, he’s found a way to combine what he was already doing and the systematic edges that the Build Alpha software can find to further increase his OVERALL edge in the markets.

I love this story for so many reasons. First, it shows that finding a trading edge is vital regardless if you choose to automate your execution or not. Second, Build Alpha is a trading tool and not necessarily a system trading tool (albeit geared toward system traders no doubt). Third, Madhur found a unique way to incorporate the old with the new to make something better – a lesson for all traders (myself included).

I have received tons of emails of Build Alpha success and thank you notes, but none as specific as this one. It is hard to market user success without the proof otherwise you all would have your doubts (rightfully so) – but after receiving this I cannot help but feel proud and share.

Congrats Madhur, who can be found on twitter here: @MadhuryAlba, and the other successful Build Alpha users out there! Thanks for pushing my development of the software and continuing to support me with this pursuit.

Also, please read all the disclaimers. I am not guaranteeing that if you license the software and poof 3 months later you are up big. No one can promise anything in this game – I just wanted to share a story that put a smile on my face and makes all the development hours worth it!

Thanks for reading,
Dave

Free Friday #19 – Long/Short Small Caps and June Update

 

This Free Friday, Free Friday #19, is a user submission! It is a long/short strategy for $IWM – the Russell 2000 ETF. Both the long and the short strategy only have two rules each and only hold for 1 day. Below I’ve posted the long strategy on the left and the short strategy on the right. Short edges have certainly been difficult to find over the past few years in the US equity indexes on a daily time frame, but one hopes they’ll pay for the effort when/if things turn south!

Both strategies were tested from 2002 to 2017 using 35% out of sample data. All performance is based on only a simple 100 shares per trade. *1 S&P500 futures contract is equivalent to about 500 $SPY shares for reference*

There is also $SPY (green plot) and $TLT (gold plot) plotted to see how the strategies would have performed on these markets as well; the strategy maintains profitability in both cases.

 

ff19_longshort

 

 

The long strategy rules are simple and all trades exit at the next day’s open.

  1. Day number is greater than 5. Today is June 30, 2017. Today’s day number is 30.
  2. High[3] <= Low[7]

 

The short strategy rules are simple as well and all trades exit at the next day’s open.

  1. Close[3] > Low[6]
  2. Close[0] > 8 Period Simple Moving Average

 

Below there is a photo of the long/short equity performance for this simple portfolio.

 

 

ff19_portfolio

 

I also want to add an update to some of the Free Friday strategies. Things were pretty quiet for most of the futures strategies other than the equity index strategies this month.

Strategies #5, #6, #16 were the only futures strategies that traded so I wanted to show their June performance below.

Nasdaq #5: +$1,640.00
Russell Futures #6: +680.00
S&P500 Futures #16: +862.50

Again, all are just trading 1 contract for demonstration purposes and were posted publicly months ago. You can see the strategies on twitter here: @dburgh

Thanks as always and have a Happy Fourth of July,

Dave

Thanks for reading,
Dave

Free Friday #18 – Building a Strategy with Open Interest

 

As always, happy Friday!

This week I was asked by a Build Alpha user if he could build strategies using a contract’s open interest. Open interest is just the total number of outstanding contracts that are held by market participants at the end of each day. So it is intuitive that as more contracts are opened or closed then it might be telling of how traders are positioning.

This is a detailed and advanced post. Build Alpha is all point and click, but this is certainly a way more advanced blog post showing how a more sophisticated user can utilize the software.

I have to admit this is not something I have looked at previously so I was quite intrigued but I pulled some open interest data from TradeStation and saved it.

I then went on to create columns I – M below. Columns I-L are momentum measures (N period change) of Open Interest. For example, column J holds the Open Interest change over the past 5 days. Column K holds the Open Interest change over the past 10 days. Column N just holds the 3 bar sum of the 1 period momentum of Open Interest. The data can be seen below opened in Excel (I know who uses Excel anymore).

 

 

excel1

 

 

In order to use the above data in Build Alpha we need to format two separate files. First, we need to create a date, open, high, low, close, volume file of the actual S&P500 futures data (columns A,C,D,E,F,G). I copy and pasted those columns to a new sheet and then reformatted the date to yyyymmdd, removed the headers, and saved it as a .csv file. Pictured below…

 

 

excel2

 

 

I then copy and pasted our dates and custom Open Interest data to a new excel sheet. This time instead of having date, open, high, low, close, volume we’ll use (copy) the date, 1 period OI change, 5 period OI change, 10 period OI change, 20 period OI change, 3 bar sum of OI as our six columns.

 

 

excel3

 

 

We can now pass this data into Build Alpha and build strategies using the intermarket capabilities. However, in this case our intermarket or Market 2 will be this custom open interest data and not some other asset.

 

 

importdata
dataimports

 

 

The two videos below show exactly how I did this process in case you didn’t follow my Thursday night, two glasses of scotch deep, blog writing.

 

 


 

 

There is a different strategy displayed in the video above, but I promised some guys on twitter I’d share the strategy I posted Thursday. So below is the actual Free Friday #18. It holds for one day and trades when these conditions are true:

  1. Momentum(OpenInterest,20)[0] <= Momentum(OpenInterest,20)[5]
  2. Momentum(OpenInterest,20)[0] > Momentum(OpenInterest,5)[1]
  3. Momentum(OpenInterest,10)[0] > Momentum(OpenInterest,10)[1]

 

 

**S&P500 Futures strategy built on Open Interest data only tested across Nasdaq, Dow Jones, and Russell futures**

**S&P500 Futures strategy built on open interest data only and tested across Nasdaq, Dow Jones, and Russell Futures. Results just based on 1 contract**

 

 

So the last rule in Build Alpha would appear as Low2[0] > Low2[1] or translated as the low of Market 2 is greater than the low of Market 2 one bar ago. However, if you remember we created a custom data series for Market 2 and in the low column we inserted the 10 period momentum of open interest!

Like I said this is a confusing post, but a really neat idea of how creative you can be with this software. The possibility of things we can test are immense.

Furthermore, when Build Alpha calculates RSI or Hurst, for example, using the close price of Market 2 (our intermarket selected) it will actually calculate RSI or Hurst on 20 bar momentum of the Open Interest (what we passed in for the close column)! You can also use the custom indicator/rule builder on these custom data columns.

You can also run strategies built on custom data like this through all the robustness and validation tests as well.

All in all, thanks for reading. I thought this was a cool idea taking system development to a whole new level.

Cheers,

Dave

Old Posts:

Thanks for reading,
Dave

Free Friday #17 – Would you trade this?

As always, happy Friday!

In this Free Friday post, I want to pose a poll question. After reading the post and viewing the graphs please respond to the poll below and I will publish the results in another post later next week.

The question is… would you trade this strategy?

First, let’s go over the strategy. The strategy was designed using GBPAUD spot data and only has three rules to determine entry. The simulation to create this strategy (and hundreds of other strategies) took less than 2 minutes.

  1. Vix[0] > Vix[1]  – Remember [1] means 1 bar ago
  2. High[4] <= Close[6]
  3. Low[6] <= High[8]

The strategy has two exit criteria. A 1.5 times 20 Period ATR profit target and 1.0 times 20 period ATR stop loss.

Here are some simple performance measures

  • January 1, 2003 to May 1, 2017 (Last 30% Out of Sample)
  • Profit $147,626.20
  • Drawdown $8,289.70
  • Win Rate 54.50%
  • Trades 198
  • Sharpe 1.78

     T-Test 3.76

Here is the strategy’s equity curve on GBPAUD. You can see the short strategy continues to perform in the out of sample period (highlighted portion of the blue line).

freefriday17_chart

I’ve also plotted how the strategy performed on three other markets. It remains profitable on Crude Oil futures, Canadian Dollar futures, and AUDUSD spot. Generally, we like to see profitability across markets and assets. However, how good is good enough to pass the test?

Next I want to share the randomized Monte Carlo test. This test re-trades the strategy 1000 times, but randomizes the exit for each entry signal. It is a test to see if we have curve-fit our exits and if our entry is strong enough to remain profitable with random exits. We can see the randomized Monte Carlo test maintains general profitability. Some fare better and some worse.

freefriday17_randomized

Next I want to share the Noise Test. This test adds and subtracts random amounts of noise (percentage of ATR) to user selected amounts of data creating 100 new price series with differing amounts of noise. The test then re-trades the strategy on these 100 new price series to see if profitability is maintained on price series with differing amounts of noise. You can see here that as we change the noise the performance degraded a bit and there are some signs of curve-fitting to the noise of the original price series.

freefriday17_noisetest

Next I want to share the forward simulator or variance testing results. In this test we simulate the strategy forward, but assume the winning percentage will degrade by 5% (user defined % in test settings). This is a useful test because things are never as rosey as our backtest results. Now we can get an idea of how things can play out in the future if the strategy were to win x% less than it did in our backtest. This is good for setting expectations of where we expect to be in the next N trades as well.

freefriday17_variancetesting

Risk of Ruin was set to $10,000 for this test. So interpreting these results… if the winning percentage in the future is 5% lower than our backtest than 23% of our simulations will have a drawdown of 10,000 or more.

This is all the information I want to provide for this poll. There are plenty more tests and information we can gather (like E-Ratio), but I want to avoid analysis by paralysis. Build Alpha licenses come with access to a 20+ video library where I explain what I look for in all the tests and features offered by Build Alpha.

If you answered yes to this poll and have a BA license then you can now generate trade-able code for MetaTrader4 in addition to the original TradeStation, MultiCharts, and NinjaTrader.

 


 

I also hope all you ES (S&P500) traders that email me caught the dip last Friday like the first Free Friday strategy did (pictured below). I posted this strategy on twitter in 2016 and it only holds for 1 day. I know a few of you have adjusted the logic and I’m hoping you caught the whole move to new highs!

ff1_winner

Happy Friday,

Dave

Email: David@buildalpha.com

Old Posts:

Thanks for reading,
Dave

Luck in Trading and Favorable Distributions

The role of luck in (algorithmic) trading is ever present. Trading is undoubtedly a field that experiences vast amounts of randomness compared to mathematical proofs or chess, for example.

That being said, a smart trader must be conscious of the possibility of outcomes and not just a single outcome. I spoke about this in my Chatwithtraders.com/103 interview, but I want to reiterate the point as I am often asked about it to this day.

The point I want to make is that it is very important to understand the distribution your trading strategy comes from and not just make decisions off the single backtest’s results.
In the interview I spoke about this graph below that shows two different trading systems that have very similar backtests. The black line on the left represents system A’s backtest and the black line on the right represents system B’s backtest. For our intents and purposes let’s assume the two individual backtest results are “similar” enough.

disto1

The colorful lines on the left is system A simulated out (can use a variety of methods such as: Monte Carlo, Bootstrapping, etc.) and the colorful lines on the right is system B simulated out using the same method. These are the possible outcomes or paths that system A and system B can take when applied to new data (Theoretically – read disclaimers about trading).

These graphs are the “distributions of outcomes” so many successful traders speak about. This picture makes it quite obvious which system you would want to trade even though system A and system B have very comparable backtests (black lines).

*There are many ways to create these “test” distributions but I will not get into specifics as Build Alpha does quite a few of them*

This second example below demonstrates this point in another way, but incorporates the role luck can have on your trading. Let’s say the blue line is the single backtest from System A (blue distribution is all possibilities). The single green line is the single backtest from System B (green possibilities).

goodvsbad2
In this graph you can see that System A (part of the blue possibilities) was lucky and performed way better than most of the possibilities and of course better than the single backtest for System B.

You can also see that System B (part of the green possibilities) was extremely unlucky and preformed way worse than most of the green possibilities.

Moving forward… do you want to count on Mother Market to give system A the same extremely favorable luck? or do you want to bet on system B’s luck evening out?

I always assume I will be close to the average of the distribution moving forward which would put us at the peaks of both of these possibilities or distributions… if that is the assumption then the choice is clear.

 


 

Build Alpha licenses now come with an instructional video series or course that goes over all the features and how to use the statistical tests the software offers. It makes spotting systems and their related distributions much easier than Build Alpha already makes it.

Thanks for reading,
Dave

3 Simple Ways To Reduce The Risk Of Curve-fitting

Curve-fitting is almost certain death for a trading or investment strategy.

So, what is curve-fitting?

Well, you know when you test a trading or investment hypothesis, fall in love with the historical results, and then the idea fails to generate similar (or even positive) returns once you decide to trade it live?

Most of the pitfalls of system trading, and trading in general, can be avoided or mitigated following these three simple techniques or rules of thumb.

  1. Use out of sample data! Out of sample data is simply withholding some of the data in your “test” period for further evaluation. For example, you have ten years of historical data and opt to put the last 30% in your back pocket. You develop a great trading strategy on the first seven years of the data set and then whip out your “out of sample” data (remaining 30% from your back pocket) and validate your findings. If the strategy fails to produce similar results in the out of sample data then you can be almost certain you have curve-fit to the first seven years of your data set. Below is chart of a strategy built using Build Alpha that highlights the out of sample period. You like to see similar growth (and characteristics) in both the in-sample and out-of-sample data.

oos_highlights2. Make sure your strategy has enough occurrences or trades. This can be simply explained with a coin flip example. If you flip a coin 10 times and it lands on heads 7 times you cannot be certain you do or do not have a rigged coin. However, if you flip a coin 10,000 times and it lands on heads 7,000 times you can have very high confidence it is indeed a rigged coin. In trading, if a strategy has 30 trades then it is unlikely you would have high confidence that what you have found is legitimate. However, if a strategy holds up over 1,000 or 3,000 plus trades then you can have higher confidence you’ve discovered true edge.

Below is a photo of only 30 coin flips and below that is a photo of six different trials of 100,000 coin flips. You can see after a large number of occurrences things tend to converge toward the true expectation (also known as the Law of Large Numbers).

smallsamples_30_3
smallsamples_100000_13. Validate your strategy across other markets. If a strategy works on only one market it has a higher chance of being curve-fit to the data set than if a strategy performs profitably on a handful of markets. I am not saying that a strategy that only works on one market is curve-fit – as there are many nuances, different players, and idiosyncrasies that exist within each market. However, if a strategy performs across markets then you can certainly have higher confidence that it is not curve-fit.

acrossmarkets

Thanks for reading,

Dave

Thanks for reading,
Dave

Python Tips – Reading Text Files, Working with dates, the command line


In this post let’s talk about two Python tutorials I put together. The first one goes over how to read in a text file, format dates, and create new columns inside a data frame. A data frame is a structure that stores your data in a convenient “table” for easy access. There are a few parts, but I will break down the code below.

The first thing we will do is import pandas library and call the built-in read_csv function. The read_csv function’s first input is the name of the file you desire to read in and store in your pandas data frame. The delimiter option allows you to specify the character that separates your text fields within your file.

import pandas as pd
df = pd.read_csv("ES.txt",delimiter=',')

Just like that we have read a text file into a pandas dataframe that we can now work with. However, if we were to plot our data frame (closing prices) now the x-axis would simply be the number of bars as we did not specify an index column. In trading and time series analysis it is often nice to have dates as your x-axis.

In the next few lines of code  I import a bulit-in python library that can read string dates (“12/30/2007”) and convert them into Python “DateTime” objects. To simplify this… we convert dates into Python dates.

I actually accomplish this by setting the built-in pandas index column to a list of newly Python formatted dates. I essentially loop through each string date, convert it, and add it to our data frame’s index.

I then delete the original string Dates.

from dateutil import parser
df.index = [parser.parse(d) for d in df['Date']]
del df['Date']

Now we can plot our closing prices and our x-axis will be dates.

df['Close'].plot()

In the code below I create a new column called “Range”. Notice how Python understands I want to do the calculation on all of the highs and lows inside our dataframe without me specifying so!

df['Range'] = df['High'] - df['Low']

Finally, the line below plots our Close and Range in two separate plots. This is from a previous tutorial video.

df[['Close','Range']].plot(subplots=True)

The second part of this tutorial is to make our lives easier. Let’s say that we wanted to run that last program on a bunch of different stocks whenever we wanted. It would be quite annoying to open up the file or notebook and change the filename in our read_csv function every time.

Instead what we can do is create a filename variable and put the filename variable inside the read_csv function. Ideally, this filename variable could be dynamically set with user input.

This code is tricky and has a few moving parts. Below is the code and then I will explain what we did.

symbol = "ES"

import sys,getopt
myopts,args = getopt.getopt(sys.argv[1:],"s")

for o,a in myopts:

if o == '-s':symbol = str(a).upper()

filename = "%s.csv" % symbol
df = pd.read_csv(filename,delimiter=',')

First, we created a symbol variable that will accept our user input. Second, we imported some built-in libraries and called the getopt function to read user input. We also specified that our desired input would be preceded by the “s” option.

We then wrote a simple for loop to read through all the command line inputs (which in this example is only one, but this template will allow you to create multiple command line input options). We then said, “if the command line option is ‘s’ then set symbol to whatever follows it”. We also morphed “whatever follows it” into an upper case, string variable.

We then set our filename variable and proceeded to read our text file into our data frame (df) as before.

This is complicated, but a major time saver. Please review the video as the extra 3 minutes might save you hours of our life by utilizing tricks like this!

Remember for those of you who don’t want to learn programming you can use research tools like Build Alpha to save even more time.

Best,

David

Thanks for reading,
Dave

Login


Username
Password
(close)

Create an Account!


Username
Email
Password
Confirm Password
Want to Login? (close)

forgot password?


Username or Email
(close)

Risk Disclosure

FUTURES AND FOREX TRADING CONTAINS SUBSTANTIAL RISK AND IS NOT FOR EVERY INVESTOR. AN INVESTOR COULD POTENTIALLY LOSE ALL OR MORE THAN THE INITIAL INVESTMENT. RISK CAPITAL IS MONEY THAT CAN BE LOST WITHOUT JEOPARDIZING ONES FINANCIAL SECURITY OR LIFE STYLE. ONLY RISK CAPITAL SHOULD BE USED FOR TRADING AND ONLY THOSE WITH SUFFICIENT RISK CAPITAL SHOULD CONSIDER TRADING. PAST PERFORMANCE IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.

Hypothetical Performance Disclaimer

HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN; IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM. ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK OF ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR TO ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL WHICH CAN ADVERSELY AFFECT TRADING RESULTS.