Beating the market with a simple supertrend strategy · AmiBroker does not directly provide any useful price data. Rather, it is a set of tools which can be used with data from a

BEATING THE MARKET WITH A SIMPLE SUPERTREND STRATEGYMATT RADTKE

MARCH 14, 2019

MATT RADTKE: BACKGROUND & EXPERIENCE

• Software development and management: 30+ years

• Trading: 12 years

• AmiBroker: 10 years

• Professional AmiBroker Researcher/Consultant: 9 years

• International client base with customers in the U.S., Canada, India, Australia,

U.K., Europe, Brazil, Indonesia

MATT RADTKE: BEAT THE MARKET WITH SUPERTREND 2

DISCLAIMER

Do not assume that the methods, techniques, or indicators presented will be

profitable or that they will not result in losses. Past results are not necessarily

indicative of future results. Examples presented are for educational purposes

only, and are not solicitations of any order to buy or sell. The author assumes no

responsibility for your trading results. There is a high degree of risk in trading.


AGENDA


AGENDA

1. AmiBroker Overview

2. Analysis: Scan, Explore, Back Test, Optimize

3. Building & Refining a Strategy

4. Robustness

a. Parameter Sensitivity

b. In Sample vs. Out of Sample testing

5. Questions & Answers


AMIBROKER OVERVIEW


AMIBROKER OVERVIEW

AmiBroker is a comprehensive technical analysis program that allows you to

quickly get started with the basics while also providing the flexibility to develop

complex trading systems.

While AmiBroker has extensive charting capabilities, it is primarily known as a

fast and powerful backtesting platform.


DATA SOURCES

AmiBroker does not directly provide any useful price data. Rather, it is a set of

tools which can be used with data from a variety of providers, including

Norgate, TIINGO, eSignal, CSI, Yahoo, Google and others.

Any data that is available in CSV format can be imported into an AmiBroker

database.

For EOD futures, forex, and US/AUS stock data, I cannot say enough good

things about Norgate Data. If you’re considering a subscription, contact me for

a discount.


ANALYSIS

SCAN, EXPLORE, BACK TEST, OPTIMIZE


ANALYSIS OVERVIEW

1. Scan: Generate Trade Signals

2. Explore: Query the Database, Calculate Indicators and Signals

3. Backtest: Simulate Actual Trades

4. Optimize: A series of backtests

a. Exhaustive

b. Smart


ANALYSIS

Before you can perform any type of analysis, you need to make selections for

each of the following:

1. Formula: Specify the AFL file containing the analysis code that you wish to

execute

2. Apply To: Will you be analyzing a single symbol, all symbols, or a filtered list

such as a watch list?

3. Range: Set the dates that you want the test results to cover

4. Settings: Long or Short? Periodicity? Pad & Align?

5. Parameters: Depending on how your script is written, you may be able to

specify input parameters without changing the AFL code.


ANALYSIS DEMO

Once you’ve defined the testing environment, you’re ready to execute one of

the four types of analysis:

1. Scan

2. Explore

3. Backtest

4. Optimize

Let’s look at how to run a backtest.



BUILDING & REFINING A STRATEGY

USING THE SUPERTREND INDICATOR TO BEAT THE MARKET


HYPOTHESES

All strategies are based on one or more hypotheses. These are the core ideas

around which the strategy is built.

For this strategy, there are two primary hypotheses:

1. NIFTY and Bank NIFTY can be profitably traded using a quantified

trend-following strategy applied to intraday bars.

2. The SuperTrend indicator is an effective way to identify the direction of

the trend.


SUPERTREND

The SuperTrend indicator is a variation on other types of volatility bands, using

a multiple of ATR to define bands above and below the current average price.

The SuperTrend line follows the lower band when the price is in an up trend (has

most recently broken the upper band), and follows the upper band when the

price is in a down trend (has most recently broken the lower band).

A more complete description as well as a function written in AFL can be found

on my web site: https://quantforhire.com/2018/08/19/supertrend-indicator/


https://quantforhire.com/2018/08/19/supertrend-indicator/

BASE STRATEGY

We will begin with a very simple set of strategy rules which will be applied to

5-minute bars for the NIFTY index. We will trade this as a cash instrument, not

as a leveraged futures product.

• Position size will be 100% of available equity when trading one

instrument, or 50% of available equity when trading two instruments

• System will trade both Long and Short

• All entries and exits take place at the open of the bar following the entry

or exit signal

• Commissions are $0.01 per share


STRATEGY RULES

1. A Long entry signal (Buy) is generated when a 5-minute bar closes above

the SuperTrend line.

2. A Long exit signal (Sell) is generated when a 5-minute bar closes below the

SuperTrend line.

3. A Short entry signal (Short) is generated when a 5-minute bar closes below

the SuperTrend line. (Same as Long exit)

4. A Short exit signal (Cover) is generated when a 5-minute bar closes above

the SuperTrend line. (Same as Long entry)


SUPERTREND-V1.AFL

#include <SuperTrendFunction.afl>

nEntryTiming = 1;

nExitTiming = 1;

nMaxPos = Param("Max Positions", 1, 1, 10, 1);

acctMargin = 100;

pctPosSize = 100/nMaxPos * 100/acctMargin;

////////////////////////////////////////////////////

// Back Test Parameters

stLengthATR = Param("SuperTrend: ATR Length", 10, 1, 100, 1);

stWidthBands = Param("SuperTrend: Band Width", 3, 1, 20, 0.1);

////////////////////////////////////////////////////

// Set up the AB environment

SetOption("CommissionMode",3);

SetOption("CommissionAmount", 0.01);

SetOption("MaxOpenPositions",nMaxPos);

SetOption("AccountMargin",acctMargin);

SetTradeDelays(nEntryTiming,nExitTiming,nEntryTiming,nExitTiming);

SetPositionSize(pctPosSize,spsPercentOfEquity);

RoundLotSize = 1;

// Initialize all arrays so the backtest will always run

Buy = Sell = Short = Cover = 0;

BuyPrice = ShortPrice = IIf(nEntryTiming==0,C,O);

SellPrice = CoverPrice = IIf(nExitTiming==0,C,O);

SuperT = SuperTrend(stLengthATR,stWidthBands);

Buy = C > SuperT;

Sell = C < SuperT;

Short = C < SuperT;

Cover = C > SuperT;MATT RADTKE: BEAT THE MARKET WITH SUPERTREND 19

VERSION 1 RESULTS


Statistics

All trades Long trades Short trades Buy&Hold ($NIFTY-NSE)

Initial capital 1000000 1000000 1000000 1000000

Ending capital 7371519.98 5849436.32 2522083.66 2085302.41

Net Profit 6371519.98 4849436.32 1522083.66 1085302.41

Net Profit % 637.15% 484.94% 152.21% 108.53%

Exposure % 99.87% 50.92% 48.96% 100.00%

Net Risk Adjusted Return % 637.96% 952.44% 310.90% 108.53%

Annual Return % 24.87% 21.70% 10.83% 8.51%

Risk Adjusted Return % 24.90% 42.62% 22.13% 8.51%

Max. system % drawdown -18.55% -11.23% -25.55% -28.07%

Total transaction costs 40904.12 20450.48 20453.64 3.84

OBSERVATIONS FROM VERSION 1

1. Compound Annual Return (CAR) has increased by 192%,

from 8.51% for B&H to 24.87% for the Long/Short strategy

2. Max Drawdown (MDD) has decreased by 34%,

from 28.07% for B&H to 18.55% for the Long/Short strategy

3. CAR/MDD of 1.34 is acceptable but could perhaps be improved by

avoiding overnight risk


VERSION 2 STRATEGY RULES

We will convert the strategy to a pure day-trading strategy (no overnight holds)

by adding earliest and lastest entry times, as well as a hard exit time. The

following new rules will be added to those established for Version 1:

1. Long and Short entry signals may only occur between 9:15am and

3:00pm

2. Long and Short exits signals will occur when crossing the SuperTrend line

(as before), OR at 3:15pm, OR on the second-to-last trading bar of the

day (to catch instances when the market closes early).


SUPERTREND-V2.AFL


…

////////////////////////////////////////////////////




tnEarliestEntry = ParamTime("Earliest Entry Time", "9:15 AM", 0);

tnLatestEntry = ParamTime("Latest Entry Time", "3:00 PM", 0);

tnFinalExit = ParamTime("Final Exit Time", "3:15 PM", 0);

…

tn = TimeNum();

dn = DateNum();

isEndOfDay = dn != Ref(dn,1);


Buy = C > SuperT AND

tn >= tnEarliestEntry AND

tn <= tnLatestEntry;

Sell = C < SuperT OR

tn >= tnFinalExit OR

Ref(isEndOfDay,nExitTiming);

Short = C < SuperT AND



Cover = C > SuperT OR



Highlights Only

Boilerplate Removed


VERSION 2 RESULTS


Statistics - Version 2


Initial capital 1000000 1000000 1000000 1000000

Ending capital 5395825.13 2118256.21 4277568.92 2085302.41

Net Profit 4395825.13 1118256.21 3277568.92 1085302.41

Net Profit % 439.58% 111.83% 327.76% 108.53%

Exposure % 95.71% 48.77% 46.94% 100.00%


Annual Return % 20.61% 8.70% 17.54% 8.51%




COMPARING VERSION 2 TO VERSION 1

1. MDD has decreased by 47%, from 18.55% for v1 to 9.87% for v2.

However, MDD for Long trades actually increased from 11.23% to 17.84%,

while MDD for Short trades decreased from 25.55% to 9.28%.

2. Compound Annual Return (CAR) has decreased by 17%, from 24.87% to

20.61%. Again, the Long trades suffered (21.70% to 8.70%) from the new

rules, while the Short trades benefited (10.83% to 17.54%).

3. CAR/MDD of 2.09 for v2 is significantly better than 1.34 for v1.


VERSION 3 STRATEGY

Based on the observation that applying day-trading constraints to the strategy

helped the Short performance while hurting the Long performance, we will test

a mixed model whereby Short trade signals include time of day constraints,

while Long trade signals do not.


SUPERTREND-V3.AFL


…

////////////////////////////////////////////////////




tnEarliestEntry = ParamTime("Earliest Entry Time", "9:15 AM", 0);

tnLatestEntry = ParamTime("Latest Entry Time", "3:00 PM", 0);

tnFinalExit = ParamTime("Final Exit Time", "3:15 PM", 0);

…

tn = TimeNum();

dn = DateNum();

isEndOfDay = dn != Ref(dn,1);


// Long trades can span multiple days, but Short trades will always

// exit on the day of entry

Buy = C > SuperT;

Sell = C < SuperT;

Short = C < SuperT AND



Cover = C > SuperT OR



Highlights Only

Boilerplate Removed


VERSION 3 RESULTS


Statistics - Version 3


Initial capital 1000000 1000000 1000000 1000000

Ending capital 15238539 8818358.13 7420180.87 2085302.41

Net Profit 14238539 7818358.13 6420180.87 1085302.41

Net Profit % 1423.85% 781.84% 642.02% 108.53%

Exposure % 97.90% 50.93% 46.96% 100.00%


Annual Return % 35.37% 27.38% 24.96% 8.51%




COMPARING VERSION 3 TO VERSION 2 AND B&H

1. Compound Annual Return (CAR) has increased by nearly 72%, from 20.61%

to 35.37%. Both Long and Short CAR improved.

2. MDD has increased slightly by 4%, from 9.87% for v2 to 10.25% for v3.

3. CAR/MDD of 3.45 for v3 is a 65% improvement over 2.09 for v2.

4. Buy & Hold metrics were:

• CAR 8.51% (315% improvement)

• MDD 28.07% (63% improvement)

• CAR/MDD 0.3 (1050% improvement)


DOES IT WORK ON BANK NIFTY?IN A WORD: YES!

VERSION 3 RESULTS FOR BANK NIFTY


Statistics - Version 3 - Bank NIFTY

All trades Long trades Short trades Buy&Hold ($BANKNIFTY-NSE)

Initial capital 1000000 1000000 1000000 1000000

Ending capital 21222815.14 16373303.9 5849511.24 3005922.15

Net Profit 20222815.14 15373303.9 4849511.24 2005922.15

Net Profit % 2022.28% 1537.33% 484.95% 200.59%

Exposure % 97.79% 50.59% 47.21% 100.00%


Annual Return % 40.45% 36.45% 21.70% 13.02%




WHAT ABOUT TRADING NIFTY AND BANK NIFTY TOGETHER?

VERSION 3 RESULTS FOR NIFTY & BANK NIFTY


Statistics - NIFTY and Bank NIFTY

All trades Long trades Short tradesBuy&Hold

($NIFTY-NSE)

Buy&Hold

($BANKNIFTY-NSE)

Initial capital 1000000 1000000 1000000 1000000 1000000

Ending capital18365264.3

2

12345209.7

57020054.57 2085302.41 3005922.15

Net Profit17365264.3

2

11345209.7

56020054.57 1085302.41 2005922.15

Net Profit % 1736.53% 1134.52% 602.01% 108.53% 200.59%

Exposure % 97.75% 50.73% 47.02% 100.00% 100.00%

Net Risk Adjusted Return

%1776.52% 2236.55% 1280.25% 108.53% 200.59%

Annual Return % 38.21% 32.24% 24.19% 8.51% 13.02%

Risk Adjusted Return % 39.09% 63.55% 51.45% 8.51% 13.02%

Max. system %

drawdown-12.63% -23.74% -16.79% -28.07% -41.27%

Total transaction costs 65127.68 28782.6 36345.08 3.84 2.21

ROBUSTNESS

WILL YOUR STRATEGY HOLD UP IN LIVE TRADING?


PARAMETER SENSITIVITY

1. Testing a variety of indicator periods, thresholds, factors, etc. is one way to protect

yourself against curve fitting or “cherry picking”.

2. Try to test a range of parameters well above and below what you think the

optimal values are.

3. Use increments and values that make sense. A 21-day moving average corresponds

to one month of data. A 23-day moving average doesn’t correspond to much of

anything. Similarly, it would be hard to explain to your trading buddy why you’re

using RSI(12) instead of RSI(10) or RSI(15).

4. You should see good performance results from a range of parameters!


IN SAMPLE & OUT OF SAMPLE TESTING

When creating and testing any data model, including trading strategies,

it is often beneficial to develop and refine the model on one set of data,

and then to validate the model on an independent data set. These are

referred to as the In-Sample (IS) and Out-Of-Sample (OOS) data sets.

Using this approach helps to avoid curve fitting.


OUT OF SAMPLE TESTING: DATE RANGE SELECTION

One common and easy way to define an OOS data set is to segregate your

data by date range, although other methods are equally valid. The key

question then becomes which date ranges to use for the IS and OOS data

sets.

Most people prefer to use the most recent data for their OOS data set. If we

assume that the market’s behavior over the next year will be more similar to

the N years immediately prior to today than it will be to the behavior from, say,

1995-2000, then using the most recent data for OOS testing is the closest we

can come to testing against future data.


LONG/SHORT ROBUSTNESS TEST

1. We will use AmiBroker’s Optimization feature to test a range of different

parameter values for the SuperTrend indicator.

• ATR Length: 5 to 30 in increments of 5, i.e. 5, 10, 15… 30

• Band Width (ATR Multiple): 1.0 to 5.0 in increments of 0.5, i.e. 1.0, 1.5, 2.0… 5.0

• AmiBroker’s exhaustive optimization will produce 6 x 9 = 54 different strategy variations

2. Our IS test period will be from 1-Jan-2010 through 31-Dec-2015.

3. Our OOS test period will be from 1-Jan-2016 through 31-Dec-2018

4. We will use combination of CAR and CAR/MDD to select the “best” IS variation

and then see how that performs in the OOS test.


IN SAMPLE RESULTS

NIFTY & BANK NIFTY

2010 - 2015


OUT OF SAMPLE RESULTS

NIFTY & BANK NIFTY

2016 - 2018


SUMMARY

1. We have built a very simple Long/Short trend following system using the

SuperTrend indicator and a few time constraints. Simpler systems often work better

in live trading than more complex ones!

2. Using selection criteria of CAR and CAR/MDD, we were able to pick an In Sample

variation of the strategy that also performed well in Out of Sample testing.

3. Now that the selection method has been validated, a logical next step would be to

run a new IS optimization on all (or nearly all) available data, and then use the

same selection criteria to determine which variation(s) to trade.


THANKS FOR ATTENDING!

QUESTIONS?

Feel free to contact me at [email protected] or visit my website.

Information and AFL for the SuperTrend indicator is available at


AFL used for the full backtest and an Excel workbook with results can be

requested by filling out the contact form at https://quantforhire.com/contact/


mailto:[email protected]


https://quantforhire.com/contact/

Documents

Beating the market with a simple supertrend strategy · AmiBroker does not directly provide any useful price data. Rather, it is a set of tools which can be used with data from a