Alpha Equivalence vs Benchmark

Outperformed S&P 500 – 19 years out of 19 years.

 

Day Trading Strategies generate positive alpha and strong gains during market meltdowns earning us the moniker ‘Crisis Alpha’. During all 10 quarters when U.S equities experienced their largest decline, Quant Savvy posted outperformance 10 out of 10 periods***. Very few investors could endure the dot.com crash & financial crash of 08. Your typical buy and hold investor has always lost money during any market crash period. We are very confident in our systems’ performance – try it yourself.

Market themes can shift promptly (COVID-19) so we keep our portfolio market neutral. 2020 has shown why a day trading long and short portfolio is the best strategy for long term performance.

Alpha Equivalence vs Benchmark

Outperformed S&P 500 – 19 years out of 19 years.

 

Day Trading Strategies generate positive alpha and strong gains during market meltdowns earning us the moniker ‘Crisis Alpha’. During all 10 quarters when U.S equities experienced their largest decline, Quant Savvy posted outperformance 10 out of 10 periods***. Very few investors could endure the dot.com crash & financial crash of 08. Your typical buy and hold investor has always lost money during any market crash period. We are very confident in our systems’ performance – try it yourself.

Market themes can shift promptly (COVID-19) so we keep our portfolio market neutral. 2020 has shown why a day trading long and short portfolio is the best strategy for long term performance.

Normalised Trades

Notes: Historical trades were all normalised to today’s current market prices. For the NQ we normalised trades to a recent market price of 15000. For the ES we normalised trades to a market price of 4350. For the YM we normalised trades to a market price of 35000. Futures are traded only in 1 contract increments. Normalising backtests and adjusting past trades to the current market price gives realistic dollar values per Emini contract.

Drawdown Calculation

Results are based on trading 1 Emini contract per trade (no compounding) and starting with an initial $100k account (this is standard inputs for representing performance). To give accurate drawdown data we assume for every new user the drawdown is from the first trade taken, so the drawdown figure is always based on $100k capital, not the historical cumulative return. Assuming the max drawdown is taken from the initial capital $100k will give a new user the very worst-case scenario. 

  • If a system report starts with $100k and records an equity peak of 200k and then a drawdown of $50k that equates to a -25% drawdown (peak to trough). However, for a new User starting with $100k who only started trading at the exact Peak of the Equity curve then they would still experience a $50k drawdown which equates to 50% of their initial capital. Our results always show the worst-case scenario and assumption that at anytime any new user will experience the worst dollar drawdown from their initial 100k capital.

Every user can adjust their position sizing to suit their risk profile, however, to standardise the performance we simply base this on $100k initial capital trading 1 Emini contract at the current market price.

Correlation

Each NQ, YM and ES system consists of many subsystems, we expect many days in which we will get correlated trades. The YM, ES and NQ can and will all go long or short on individual days. Also, you have the potential for multiple longs or shorts on individual markets all at different prices.

Trade Size Expectations

Rare days in backtesting confirm the potential to be Long/Short 8 subsystems (each NQ, ES and YM system is comprised of multiple subsystems). Users trading a fixed position size of 1 Emini contract can potentially have as an example +4 Long YM, +2 Long ES, +2 Long NQ = total Emini +8 across three different markets all at the same time on the same day (each subsystem will have separate entry times, stops and targets). If you set risk of 2% per trade on your initial capital then on the rare correlated trading days you have the potential risk of 12%.

Days with +8 Emini simultaneously are rare and have only occurred a handful of times in backtesting from 2002 to 2021. However, we do expect +4 Emini contracts in the same direction on an individual day to occur a handful of times per year (average4 times per year). Because the subsystems have different entry times, stops and targets and trading three different markets, many of these correlated trade days will not have highly correlated daily profit and loss.

Users should understand they can have more than one contract in the same direction on the same market and must take this into account when selecting their position size. Users should also take the broker margin required when selecting position size.

Normalised Trades

Notes: Historical trades were all normalised to today’s current market prices. For the NQ we normalised trades to a recent market price of 15000. For the ES we normalised trades to a market price of 4350. For the YM we normalised trades to a market price of 35000. Futures are traded only in 1 contract increments. Normalising backtests and adjusting past trades to the current market price gives realistic dollar values per Emini contract.

Drawdown Calculation

Results are based on trading 1 Emini contract per trade (no compounding) and starting with an initial $100k account (this is standard inputs for representing performance). To give accurate drawdown data we assume for every new user the drawdown is from the first trade taken, so the drawdown figure is always based on $100k capital, not the historical cumulative return. Assuming the max drawdown is taken from the initial capital $100k will give a new user the very worst-case scenario. 

  • If a system report starts with $100k and records an equity peak of 200k and then a drawdown of $50k that equates to a -25% drawdown (peak to trough). However, for a new User starting with $100k who only started trading at the exact Peak of the Equity curve then they would still experience a $50k drawdown which equates to 50% of their initial capital. Our results always show the worst-case scenario and assumption that at anytime any new user will experience the worst dollar drawdown from their initial 100k capital.

Every user can adjust their position sizing to suit their risk profile, however, to standardise the performance we simply base this on $100k initial capital trading 1 Emini contract at the current market price.

Correlation

Each NQ, YM and ES system consists of many subsystems, we expect many days in which we will get correlated trades. The YM, ES and NQ can and will all go long or short on individual days. Also, you have the potential for multiple longs or shorts on individual markets all at different prices.

Trade Size Expectations

Rare days in backtesting confirm the potential to be Long/Short 8 subsystems (each NQ, ES and YM system is comprised of multiple subsystems). Users trading a fixed position size of 1 Emini contract can potentially have as an example +4 Long YM, +2 Long ES, +2 Long NQ = total Emini +8 across three different markets all at the same time on the same day (each subsystem will have separate entry times, stops and targets). If you set risk of 2% per trade on your initial capital then on the rare correlated trading days you have the potential risk of 12%.

Days with +8 Emini simultaneously are rare and have only occurred a handful of times in backtesting from 2002 to 2021. However, we do expect +4 Emini contracts in the same direction on an individual day to occur a handful of times per year (average4 times per year). Because the subsystems have different entry times, stops and targets and trading three different markets, many of these correlated trade days will not have highly correlated daily profit and loss.

Users should understand they can have more than one contract in the same direction on the same market and must take this into account when selecting their position size. Users should also take the broker margin required when selecting position size.

PROFIT FACTOR

Every $ 1 Invested, Alpha Equivalence $ 1.64

 

Net profit does not accurately define performance. You want to know how much you make for every dollar you risk? A profit factor of less than 1 means a losing system. A profit factor between 1.2 and 1.5 is a system we would never trade. However, a profit factor above 1.6 is considered exceptional and gives a high degree of confidence for long term success. The high-profit factor for each Alpha Equivalence system** presents reliable evidence of robustness.

CFTC RULE 4.41 – Hypothetical or simulated performance results have certain limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under — or over — compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown.

PROFIT FACTOR

Every $ 1 Invested, Alpha Equivalence $ 1.64

 

Net profit does not accurately define performance. You want to know how much you make for every dollar you risk? A profit factor of less than 1 means a losing system. A profit factor between 1.2 and 1.5 is a system we would never trade. However, a profit factor above 1.6 is considered exceptional and gives a high degree of confidence for long term success. The high-profit factor for each Alpha Equivalence system** presents reliable evidence of robustness.

CFTC RULE 4.41 – Hypothetical or simulated performance results have certain limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under — or over — compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown.

MAXIMUM DRAWDOWN

Alpha Equivalence max drawdown  33.21%.

The greatest movement from equity high to a low point is called a drawdown. Small drawdowns allow investors to withstand periods of weak performance with confidence. Quant risk management is critical, no matter how good an automated trading system is, there will be a period where there is a losing streak. The average yearly return of 103.56% is divided by the max drawdown of -33.21% = 3.12 (the average yearly return is three times the max ever recorded drawdown).

A good quant trading system should break new equity highs every quarter so investors are not stuck in a hole with no profits for extended periods. These are Day Trading Strategies only which eliminates considerable overnight market risk.

MAXIMUM DRAWDOWN

Alpha Equivalence max drawdown  33.21%.

The greatest movement from equity high to a low point is called a drawdown. Small drawdowns allow investors to withstand periods of weak performance with confidence. Quant risk management is critical, no matter how good an automated trading system is, there will be a period where there is a losing streak. The average yearly return of 103.56% is divided by the max drawdown of -33.21% = 3.12 (the average yearly return is three times the max ever recorded drawdown).

A good quant trading system should break new equity highs every quarter so investors are not stuck in a hole with no profits for extended periods. These are Day Trading Strategies only which eliminates considerable overnight market risk.

LONG-TERM PERFORMANCE

Don’t buy and hold! Use Alpha Equivalence for redefining your financial future.

Newbie investors buy and hold – this trading style never has consistent monthly returns and one smart money seldom follows! Alpha Equivalence Bot is a portfolio of 19 sub systems, completely individualistic leading to long-term performance**. We are very confident in our systems’ performance – try it yourself.

The table (left) shows very few losing months and consistent monthly returns.

Pos BotJanFebMarAprMayJunJulAugSepOctNovDecYearAvg Monthly
20210.4%-14.0%12.7%19.1%16.4%1.5%4.6%16.4%57.2%7.15%
20206.1%23.2%28.3%6.6%11.2%6.1%25.1%0.3%-7.1%17.9%-3.5%5.3%119.5%9.96%
20198.3%16.5%0.4%8.5%15.5%17.7%4.5%9.1%5.4%13.9%3.6%14.3%117.5%9.80%
201810.0%-4.9%-11.2%15.7%13.5%0.2%13.0%14.8%7.2%26.9%9.4%4.8%99.6%8.30%
2017-6.1%4.0%-3.2%9.7%6.9%0.0%9.3%7.9%-0.8%3.1%9.2%-4.9%35.0%2.92%

LONG-TERM PERFORMANCE

Don’t buy and hold! Use Alpha Equivalence for redefining your financial future.

Newbie investors buy and hold – this trading style never has consistent monthly returns and one smart money seldom follows! Alpha Equivalence Bot is a portfolio of 19 sub systems, completely individualistic leading to long-term performance**. We are very confident in our systems’ performance – try it yourself.

The table (left) shows very few losing months and consistent monthly returns.

Pos BotJanFebMarAprMayJunJulAugSepOctNovDecYearAvg Monthly
20210.4%-14.0%12.7%19.1%16.4%1.5%4.6%16.4%57.2%7.15%
20206.1%23.2%28.3%6.6%11.2%6.1%25.1%0.3%-7.1%17.9%-3.5%5.3%119.5%9.96%
20198.3%16.5%0.4%8.5%15.5%17.7%4.5%9.1%5.4%13.9%3.6%14.3%117.5%9.80%
201810.0%-4.9%-11.2%15.7%13.5%0.2%13.0%14.8%7.2%26.9%9.4%4.8%99.6%8.30%
2017-6.1%4.0%-3.2%9.7%6.9%0.0%9.3%7.9%-0.8%3.1%9.2%-4.9%35.0%2.92%

MONTE CARLO ANALYSIS

Shuffling Trades Simulations for Expected Risk

Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted. It is a technique used to understand the impact of risk and uncertainty in automated trading systems. It produces an estimation of expected drawdowns and checks for possible worst-case scenarios.

We applied 1000 Monte Carlo simulations to the Alpha Equivalence algorithmic system. Shuffling trades over 1000 simulations give us the best and worse case for drawdown and sequential wins and losses.

Smallest drawdown -$14,045

Base drawdown -$33,660

Largest drawdown -$41,243

Monte Carlo is a probability test, not a certainty test, and assumes returns of markets follow a Gaussian normal distribution. However, in reality, financial markets might experience huge percentage changes that are outside Gaussian curve, which Monte Carlo analysis cannot predict.

MONTE CARLO ANALYSIS

Shuffling Trades Simulations for Expected Risk

Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted. It is a technique used to understand the impact of risk and uncertainty in automated trading systems. It produces an estimation of expected drawdowns and checks for possible worst-case scenarios.

We applied 1000 Monte Carlo simulations to the Alpha Equivalence algorithmic system. Shuffling trades over 1000 simulations give us the best and worse case for drawdown and sequential wins and losses.

Smallest drawdown -$14,045

Base drawdown -$33,660

Largest drawdown -$41,243

Monte Carlo is a probability test, not a certainty test, and assumes returns of markets follow a Gaussian normal distribution. However, in reality, financial markets might experience huge percentage changes that are outside Gaussian curve, which Monte Carlo analysis cannot predict.

We are very confident in our systems' performance - try it now. We pay for your server, hardware & software! We pay for server maintenance & hardware monitoring

TOP TRADES, DRAWDOWNS AND SERIES

Top 10 Largest Wins %DateTop 10 Largest Losses %Date
7.01%01/12/2008-4.08%21/03/2017
5.94%25/02/2013-4.07%27/07/2017
5.93%10/10/2018-4.07%07/11/2013
5.50%29/01/2002-4.02%17/08/2017
5.34%01/12/2008-3.64%05/07/2006
5.27%13/11/2013-3.60%12/05/2004
4.97%25/03/2015-3.38%25/02/2013
4.77%25/02/2013-3.18%07/04/2003
4.75%25/02/2020-2.96%13/03/2018
4.72%13/11/2008-2.79%11/06/2020
Top 10 Largest Wins $DateTop 10 Largest Losses $Date
$7,008.07 01/12/2008 $-4,081.28 21/03/2017
$5,944.83 25/02/2013 $-4,072.27 27/07/2017
$5,930.37 10/10/2018 $-4,071.66 07/11/2013
$5,497.79 29/01/2002 $-4,019.44 17/08/2017
$5,336.35 01/12/2008 $-3,636.83 05/07/2006
$5,269.41 13/11/2013 $-3,595.10 12/05/2004
$4,970.65 25/03/2015 $-3,375.96 25/02/2013
$4,772.72 25/02/2013 $-3,176.08 07/04/2003
$4,750.98 25/02/2020 $-2,959.02 13/03/2018
$4,721.96 13/11/2008 $-2,785.31 11/06/2020
Top 60 Wins Per Day %DateBottom 60 Losses Per Day %Date
16.74%01/12/2008-10.45%21/03/2017
12.23%24/10/2018-9.67%04/05/2018
12.08%04/08/2011-8.28%10/01/2008
11.97%23/02/2009-8.25%30/11/2005
11.58%07/07/2009-8.24%25/09/2020
11.52%21/02/2003-8.21%13/03/2018
11.39%25/02/2020-8.05%10/04/2012
10.96%06/05/2010-7.87%04/10/2006
10.43%25/07/2003-7.70%13/12/2012
10.07%28/11/2012-7.67%30/06/2009
9.77%01/07/2003-7.56%03/12/2009
9.52%13/11/2008-7.40%24/07/2013
9.50%16/08/2007-7.11%03/12/2015
9.38%06/06/2012-6.93%17/08/2017
9.29%31/01/2020-6.91%19/12/2011
9.23%18/10/2011-6.86%11/02/2011
9.22%19/03/2004-6.77%22/05/2013
9.08%30/07/2020-6.69%10/05/2007
8.94%09/12/2002-6.66%25/02/2008
8.84%25/02/2013-6.64%03/01/2005
8.46%10/10/2002-6.61%10/07/2012
8.16%10/12/2014-6.54%23/03/2018
8.13%07/05/2019-6.43%21/05/2002
8.10%29/09/2008-6.40%18/10/2019
8.10%09/10/2014-6.39%24/02/2021
8.08%12/07/2007-6.37%02/01/2008
8.06%02/02/2015-6.28%24/08/2012
7.98%24/07/2012-6.26%27/08/2013
7.97%28/04/2015-6.21%07/11/2013
7.82%03/10/2019-6.11%30/08/2004
7.77%18/09/2020-6.01%28/01/2004
7.76%02/12/2019-6.00%03/02/2009
7.71%23/10/2008-6.00%02/12/2015
7.64%16/04/2008-5.99%18/05/2011
7.64%02/01/2003-5.96%30/01/2009
7.56%03/02/2014-5.82%20/11/2002
7.48%10/10/2018-5.82%20/11/2019
7.42%16/05/2007-5.76%27/03/2019
7.34%01/04/2008-5.72%31/12/2014
7.20%03/01/2006-5.58%07/03/2003
7.11%05/08/2003-5.55%27/07/2017
7.07%10/03/2003-5.50%23/09/2005
6.92%24/06/2002-5.49%05/03/2018
6.88%22/10/2020-5.44%12/05/2008
6.84%20/07/2020-5.19%13/03/2007
6.81%12/01/2005-5.16%04/01/2005
6.80%17/09/2008-5.13%21/10/2002
6.78%10/04/2014-5.08%30/06/2016
6.77%08/10/2014-5.07%17/10/2006
6.73%30/10/2009-5.06%17/05/2017
6.66%07/07/2010-5.01%23/02/2016
6.63%13/03/2003-4.98%07/04/2006
6.61%27/02/2019-4.95%28/07/2015
6.55%19/11/2008-4.87%25/10/2012
6.52%25/05/2004-4.85%09/02/2005
6.50%02/04/2018-4.84%29/07/2010
6.49%21/12/2020-4.83%27/11/2018
6.48%11/05/2017-4.83%07/12/2006
6.47%03/08/2007-4.82%04/10/2005
6.45%29/12/2008-4.78%03/09/2020
Top 60 Wins Per Day $DateBottom 60 Losses Per Day $Date
$16,74001/12/2008-$10,45321/03/2017
$12,23324/10/2018-$9,67404/05/2018
$12,07704/08/2011-$8,28010/01/2008
$11,97523/02/2009-$8,25430/11/2005
$11,57607/07/2009-$8,23925/09/2020
$11,51821/02/2003-$8,21013/03/2018
$11,38525/02/2020-$8,05510/04/2012
$10,96306/05/2010-$7,86504/10/2006
$10,43225/07/2003-$7,69813/12/2012
$10,06828/11/2012-$7,66830/06/2009
$9,77001/07/2003-$7,55703/12/2009
$9,51913/11/2008-$7,39624/07/2013
$9,49916/08/2007-$7,10703/12/2015
$9,38106/06/2012-$6,93017/08/2017
$9,29131/01/2020-$6,91419/12/2011
$9,22818/10/2011-$6,85711/02/2011
$9,21719/03/2004-$6,76822/05/2013
$9,08230/07/2020-$6,68810/05/2007
$8,93709/12/2002-$6,65725/02/2008
$8,83625/02/2013-$6,63803/01/2005
$8,46210/10/2002-$6,60910/07/2012
$8,16010/12/2014-$6,54223/03/2018
$8,12607/05/2019-$6,43121/05/2002
$8,10129/09/2008-$6,40518/10/2019
$8,09709/10/2014-$6,38824/02/2021
$8,07512/07/2007-$6,37202/01/2008
$8,06202/02/2015-$6,28124/08/2012
$7,98224/07/2012-$6,26027/08/2013
$7,96928/04/2015-$6,21507/11/2013
$7,82403/10/2019-$6,11430/08/2004
$7,77018/09/2020-$6,01528/01/2004
$7,76102/12/2019-$6,00503/02/2009
$7,70923/10/2008-$5,99602/12/2015
$7,64116/04/2008-$5,98618/05/2011
$7,63902/01/2003-$5,96430/01/2009
$7,55703/02/2014-$5,82520/11/2002
$7,47610/10/2018-$5,82520/11/2019
$7,42416/05/2007-$5,76227/03/2019
$7,34001/04/2008-$5,72531/12/2014
$7,20003/01/2006-$5,58007/03/2003
$7,11005/08/2003-$5,54927/07/2017
$7,07410/03/2003-$5,50123/09/2005
$6,91624/06/2002-$5,49005/03/2018
$6,88222/10/2020-$5,44212/05/2008
$6,84420/07/2020-$5,19513/03/2007
$6,81112/01/2005-$5,16004/01/2005
$6,80317/09/2008-$5,12721/10/2002
$6,77810/04/2014-$5,07930/06/2016
$6,77308/10/2014-$5,06717/10/2006
$6,72930/10/2009-$5,05817/05/2017
$6,66307/07/2010-$5,01023/02/2016
$6,63213/03/2003-$4,98107/04/2006
$6,60527/02/2019-$4,94528/07/2015
$6,55219/11/2008-$4,87225/10/2012
$6,52125/05/2004-$4,84809/02/2005
$6,50002/04/2018-$4,84129/07/2010
$6,49021/12/2020-$4,82927/11/2018
$6,48511/05/2017-$4,82607/12/2006
$6,47403/08/2007-$4,81504/10/2005
$6,44829/12/2008-$4,78203/09/2020
Max Drawdown %Max Drawdown DateStartedRecoveredDays
-33.21%13/10/200523/09/200505/04/2006139
-28.16%11/03/200428/01/200413/05/200477
-26.10%18/02/201003/12/200922/05/2010122
-20.93%03/11/200604/10/200602/12/200643
-17.82%18/05/201213/04/201207/06/201240
-17.39%21/11/200231/10/200218/12/200235
-17.24%08/06/200630/05/200612/07/200632
-17.23%18/10/200421/09/200408/12/200457
-15.72%08/09/200827/08/200830/09/200825
-15.62%17/09/200205/08/200228/09/200240
Winning Series1234567891011
Number Winning Series245015991085714461313191129896240
Losing Series1234567891011
Number Losing Series2450140683248728316410057

TOP TEN TRADES, DRAWDOWNS AND SERIES

Top 10 Largest Wins %DateTop 10 Largest Losses %Date
7.01%01/12/2008-4.08%21/03/2017
5.94%25/02/2013-4.07%27/07/2017
5.93%10/10/2018-4.07%07/11/2013
5.50%29/01/2002-4.02%17/08/2017
5.34%01/12/2008-3.64%05/07/2006
5.27%13/11/2013-3.60%12/05/2004
4.97%25/03/2015-3.38%25/02/2013
4.77%25/02/2013-3.18%07/04/2003
4.75%25/02/2020-2.96%13/03/2018
4.72%13/11/2008-2.79%11/06/2020
Top 10 Largest Wins $DateTop 10 Largest Losses $Date
$7,008.07 01/12/2008 $-4,081.28 21/03/2017
$5,944.83 25/02/2013 $-4,072.27 27/07/2017
$5,930.37 10/10/2018 $-4,071.66 07/11/2013
$5,497.79 29/01/2002 $-4,019.44 17/08/2017
$5,336.35 01/12/2008 $-3,636.83 05/07/2006
$5,269.41 13/11/2013 $-3,595.10 12/05/2004
$4,970.65 25/03/2015 $-3,375.96 25/02/2013
$4,772.72 25/02/2013 $-3,176.08 07/04/2003
$4,750.98 25/02/2020 $-2,959.02 13/03/2018
$4,721.96 13/11/2008 $-2,785.31 11/06/2020
Max Drawdown %Max Drawdown DateStartedRecoveredDays
-33.21%13/10/200523/09/200505/04/2006139
-28.16%11/03/200428/01/200413/05/200477
-26.10%18/02/201003/12/200922/05/2010122
-20.93%03/11/200604/10/200602/12/200643
-17.82%18/05/201213/04/201207/06/201240
-17.39%21/11/200231/10/200218/12/200235
-17.24%08/06/200630/05/200612/07/200632
-17.23%18/10/200421/09/200408/12/200457
-15.72%08/09/200827/08/200830/09/200825
-15.62%17/09/200205/08/200228/09/200240
Winning Series1234567891011
Number Winning Series245015991085714461313191129896240
Losing Series1234567891011
Number Losing Series2450140683248728316410057

TRADE ANALYSIS

Yearly # Trades, Monthly # Trades

Alpha Equivalence has over 13089 trades, which, statistically, decreases our chances not to make money**. Algorithmic system trades should be Long and Short otherwise there will be a long bias and the algorithmic trading bot will fail every time the market plunges. Alpha Equivalence is balanced long and short and market neutral.

Alpha Equivalence averages 58 trades per month and 701 trades per year. Our research shows that day trading markets are 90% random, only failed human day traders think they can pick every minor wave. Alpha Equivalence is very selective and trades only when the odds are heavily stacked in its favour. Overtrading is a human day trader construct and one algo bots will never succumb to. Experience the advantages of algorithmic trading yourself.

TRADE ANALYSIS

Yearly # Trades, Monthly # Trades

Alpha Equivalence has over 13089 trades, which, statistically, decreases our chances not to make money**. Algorithmic system trades should be Long and Short otherwise there will be a long bias and the algorithmic trading bot will fail every time the market plunges. Alpha Equivalence is balanced long and short and market neutral.

Alpha Equivalence averages 58 trades per month and 701 trades per year. Our research shows that day trading markets are 90% random, only failed human day traders think they can pick every minor wave. Alpha Equivalence is very selective and trades only when the odds are heavily stacked in its favour. Overtrading is a human day trader construct and one algo bots will never succumb to. Experience the advantages of algorithmic trading yourself.

SCATTER PLOTS

Highly Performing Investment

The % scatterplot highlights that very few trades on the downside exceed -2% and none less than -3%. On the upside, we have many trades exceeding 3% gains. The risk to reward on each trade is positive as is the overall trades winning percentage of 54%.

The $ scatterplot shows very few trades exceed losses of -$1k on the downside but numerous trades are greater than +$1k on the upside. Keep in mind the dollar value will increase relative to an increase in market price: see normalised trade data.

SCATTER PLOTS

Highly Performing Investment

The % scatterplot highlights that very few trades on the downside exceed -2% and none less than -3%. On the upside, we have many trades exceeding 3% gains. The risk to reward on each trade is positive as is the overall trades winning percentage of 54%.

The $ scatterplot shows very few trades exceed losses of -$1k on the downside but numerous trades are greater than +$1k on the upside. Keep in mind the dollar value will increase relative to an increase in market price: see normalised trade data.

INITIAL CAPITAL RISK PROFILES

See expectations based on different risk profiles – each investor has different risk tolerance and profiles should be adjusted relative to market price and volatility. Futures Trading has large potential rewards but also large potential risks.

It is not appropriate for all investors – do not trade with money you cannot afford to lose.

Results based on Intial Capital$20,000$50,000$100,000$200,000$300,000
2021$4,210 $15,952 $28,624 $57,164 $84,735
2020$11,693 $28,341 $62,811 $119,524 $183,759
2019$12,819 $29,197 $59,604 $117,548 $177,546
2018$6,620 $25,968 $47,324 $99,558 $148,686
2017$3,616 $8,943 $17,798 $35,002 $52,882
Net Gain Since 2002$189,222 $482,299 $967,924 $1,933,122 $2,905,425
$ Avg Monthly Gain$845 $2,153 $4,321 $8,630 $12,971
% Avg Monthly Gain4.22%4.31%4.32%4.32%4.32%
% Avg Annual Gain50.68%51.67%51.85%51.78%51.88%
Avg Winning Trade$76 $196 $392 $785 $1,177
Avg Losing Trade-$65 -$160 -$319 -$639 -$958
Avg Winning Day$ 168$ 413$ 822$ 1,645$ 2,470
Avg Losing Day-$ 126-$ 327-$ 655-$ 1,312-$ 1,961
Max Drawdown-$3,522 -$8,416 -$16,712 -$33,213 -$50,036
Max Drawdown %-17.61%-16.83%-16.71%-16.61%-16.68%
Trades Per Month5858585858
Total All Systems Trades1308913089130891308913089
Results based on Intial Capital $20,000 $50,000 $100,000 $200,000 $300,000
2021 $12,031 $28,623 $57,163 $116,351$172,018
2020 $20,509 $62,810 $119,523 $242,973$362,516
2019 $22,257 $59,604 $117,547 $234,955$352,797
2018 $20,144 $47,324 $99,557 $199,075$298,948
2017 $6,859 $17,797 $35,001 $70,645$105,619
Net Gain Since 2002 $382,588 $967,923 $1,933,121 $3,874,246$5,803,525
$ Avg Monthly Gain $1,708 $4,321 $8,630 $17,296$25,909
% Avg Monthly Gain8.54%8.64%8.63%8.65%8.64%
% Avg Annual Gain 102.48%103.71%103.56%103.77%103.63%
Avg Winning Trade $157 $392 $785 $1,569$2,354
Avg Losing Trade $-129 $-319 $-639 $-1,276-$1,915
Avg Winning Day $ 332 $ 822$ 1645 $ 3,292$ 4,939
Avg Losing Day -$ 263 -$ 655 -$ 1,312 -$ 2,616-$ 3,925
Max Drawdown $-6,503 $-16,712 -$33,212 $-66,703-$100,005
Max Drawdown %-32.52%-33.42%-33.21%-33.35%-33.33%
Trades Per Month5858585858
Total All Systems Trades13,08913,08913,08913,08913,089
Results based on Intial Capital$20,000$50,000$100,000$200,000$300,000
2021$16,790 $43,860 $84,735 $172,018 $257,725
2020$38,949 $88,754 $183,759 $362,516 $544,892
2019$34,230 $87,430 $177,546 $352,797 $529,663
2018$27,455 $74,642 $148,686 $298,948 $446,036
2017$10,531 $26,164 $52,882 $105,619 $158,348
Net Gain Since 2002$579,465 $1,449,138 $2,905,425 $5,803,525 $8,705,932
$ Avg Monthly Gain$2,587 $6,469 $12,971 $25,909 $38,866
% Avg Monthly Gain12.93%12.94%12.97%12.95%12.96%
% Avg Annual Gain155.21%155.26%155.65%155.45%155.46%
Avg Winning Trade$235 $588 $1,177 $2,354 $3,530
Avg Losing Trade-$192 -$479 -$958 -$1,915 -$2,873
Avg Winning Day$ 493$ 1,233$ 2,471$ 4,939$ 7,406
Avg Losing Day-$ 395-$ 984-$ 1,962-$ 3,925-$ 5,890
Max Drawdown-$9,930 -$24,939 -$50,036 -$100,005 -$150,101
Max Drawdown %-49.65%-49.88%-50.04%-50.00%-50.03%
Trades Per Month5858585858
Total All Systems Trades1308913089130891308913089
Results based on Intial Capital$20,000$50,000$100,000$200,000$300,000
2021$24,215 $57,164 $116,351 $229,127 $346,706
2020$49,981 $119,524 $242,974 $484,131 $727,377
2019$46,857 $117,548 $234,955 $470,128 $704,365
2018$41,996 $99,558 $199,075 $396,300 $595,128
2017$13,808 $35,002 $70,645 $141,184 $211,517
Net Gain Since 2002$780,155 $1,933,122 $3,874,246 $7,736,607 $11,609,140
$ Avg Monthly Gain$3,483 $8,630 $17,296 $34,538 $51,827
% Avg Monthly Gain17.41%17.26%17.30%17.27%17.28%
% Avg Annual Gain208.97%207.12%207.55%207.23%207.31%
Avg Winning Trade$315 $785 $1,569 $3,137 $4,707
Avg Losing Trade-$256 -$639 -$1,276 -$2,552 -$3,830
Avg Winning Day$ 660$ 1,645$ 3,292$ 6,578$ 9,870
Avg Losing Day-$ 524-$ 1,312-$ 2,616-$ 5,237-$ 7,857
Max Drawdown-$13,373 -$33,213 -$66,703 -$133,328 -$199,984
Max Drawdown %-66.86%-66.43%-66.70%-66.66%-66.66%
Trades Per Month5858585858
Total All Systems Trades1308913089130891308913089

INITIAL CAPITAL RISK PROFILES

See expectations based on different risk profiles – each investor has different risk tolerance and profiles should be adjusted relative to market price and volatility. Futures Trading has large potential rewards but also large potential risks.

It is not appropriate for all investors – do not trade with money you cannot afford to lose.

Results based on Intial Capital$20,000$50,000$100,000$200,000$300,000
2021$4,210 $15,952 $28,624 $57,164 $84,735
2020$11,693 $28,341 $62,811 $119,524 $183,759
2019$12,819 $29,197 $59,604 $117,548 $177,546
2018$6,620 $25,968 $47,324 $99,558 $148,686
2017$3,616 $8,943 $17,798 $35,002 $52,882
Net Gain Since 2002$189,222 $482,299 $967,924 $1,933,122 $2,905,425
$ Avg Monthly Gain$845 $2,153 $4,321 $8,630 $12,971
% Avg Monthly Gain4.22%4.31%4.32%4.32%4.32%
% Avg Annual Gain50.68%51.67%51.85%51.78%51.88%
Avg Winning Trade$76 $196 $392 $785 $1,177
Avg Losing Trade-$65 -$160 -$319 -$639 -$958
Avg Winning Day$ 168$ 413$ 822$ 1,645$ 2,470
Avg Losing Day-$ 126-$ 327-$ 655-$ 1,312-$ 1,961
Max Drawdown-$3,522 -$8,416 -$16,712 -$33,213 -$50,036
Max Drawdown %-17.61%-16.83%-16.71%-16.61%-16.68%
Trades Per Month5858585858
Total All Systems Trades1308913089130891308913089
Results based on Intial Capital $20,000 $50,000 $100,000 $200,000 $300,000
2021 $12,031 $28,623 $57,163 $116,351$172,018
2020 $20,509 $62,810 $119,523 $242,973$362,516
2019 $22,257 $59,604 $117,547 $234,955$352,797
2018 $20,144 $47,324 $99,557 $199,075$298,948
2017 $6,859 $17,797 $35,001 $70,645$105,619
Net Gain Since 2002 $382,588 $967,923 $1,933,121 $3,874,246$5,803,525
$ Avg Monthly Gain $1,708 $4,321 $8,630 $17,296$25,909
% Avg Monthly Gain8.54%8.64%8.63%8.65%8.64%
% Avg Annual Gain 102.48%103.71%103.56%103.77%103.63%
Avg Winning Trade $157 $392 $785 $1,569$2,354
Avg Losing Trade $-129 $-319 $-639 $-1,276-$1,915
Avg Winning Day $ 332 $ 822$ 1645 $ 3,292$ 4,939
Avg Losing Day -$ 263 -$ 655 -$ 1,312 -$ 2,616-$ 3,925
Max Drawdown $-6,503 $-16,712 -$33,212 $-66,703-$100,005
Max Drawdown %-32.52%-33.42%-33.21%-33.35%-33.33%
Trades Per Month5858585858
Total All Systems Trades13,08913,08913,08913,08913,089
Results based on Intial Capital$20,000$50,000$100,000$200,000$300,000
2021$16,790 $43,860 $84,735 $172,018 $257,725
2020$38,949 $88,754 $183,759 $362,516 $544,892
2019$34,230 $87,430 $177,546 $352,797 $529,663
2018$27,455 $74,642 $148,686 $298,948 $446,036
2017$10,531 $26,164 $52,882 $105,619 $158,348
Net Gain Since 2002$579,465 $1,449,138 $2,905,425 $5,803,525 $8,705,932
$ Avg Monthly Gain$2,587 $6,469 $12,971 $25,909 $38,866
% Avg Monthly Gain12.93%12.94%12.97%12.95%12.96%
% Avg Annual Gain155.21%155.26%155.65%155.45%155.46%
Avg Winning Trade$235 $588 $1,177 $2,354 $3,530
Avg Losing Trade-$192 -$479 -$958 -$1,915 -$2,873
Avg Winning Day$ 493$ 1,233$ 2,471$ 4,939$ 7,406
Avg Losing Day-$ 395-$ 984-$ 1,962-$ 3,925-$ 5,890
Max Drawdown-$9,930 -$24,939 -$50,036 -$100,005 -$150,101
Max Drawdown %-49.65%-49.88%-50.04%-50.00%-50.03%
Trades Per Month5858585858
Total All Systems Trades1308913089130891308913089
Results based on Intial Capital$20,000$50,000$100,000$200,000$300,000
2021$24,215 $57,164 $116,351 $229,127 $346,706
2020$49,981 $119,524 $242,974 $484,131 $727,377
2019$46,857 $117,548 $234,955 $470,128 $704,365
2018$41,996 $99,558 $199,075 $396,300 $595,128
2017$13,808 $35,002 $70,645 $141,184 $211,517
Net Gain Since 2002$780,155 $1,933,122 $3,874,246 $7,736,607 $11,609,140
$ Avg Monthly Gain$3,483 $8,630 $17,296 $34,538 $51,827
% Avg Monthly Gain17.41%17.26%17.30%17.27%17.28%
% Avg Annual Gain208.97%207.12%207.55%207.23%207.31%
Avg Winning Trade$315 $785 $1,569 $3,137 $4,707
Avg Losing Trade-$256 -$639 -$1,276 -$2,552 -$3,830
Avg Winning Day$ 660$ 1,645$ 3,292$ 6,578$ 9,870
Avg Losing Day-$ 524-$ 1,312-$ 2,616-$ 5,237-$ 7,857
Max Drawdown-$13,373 -$33,213 -$66,703 -$133,328 -$199,984
Max Drawdown %-66.86%-66.43%-66.70%-66.66%-66.66%
Trades Per Month5858585858
Total All Systems Trades1308913089130891308913089

Normalising trades for expected dollar values

Drawdown metrics can be misleading when analysing system performance. Every investor wants to know what they can potentially lose or what a system has lost in the past, this way the investor has some expectation going forwards. This is a measure of risk and survivability, especially when you consider margins (futures are traded heavily on margin).

Drawdown percentage depends entirely on the starting capital. Most systems backtest reports are run with the assumption of starting $100k capital trading 1 contract with no compounding of returns. However, some developers will then inform investors that the system can be run with as little as $10k and still have the same performance.

Let’s assume the backtest report stated a 10% drawdown on $100k right of the start of trading = $10k drawdown.

Traders starting this with $35k the dollar value drawdown will be -$8211 trading 1 futures contract. Percentage drawdown is  23.46%. 

Futures are traded only in 1 contract increments. Markets move in percentages and price has risen remarkably over the last decade. It is better to normalise the trade data to the current market price to get a more accurate figure for a trades expected dollar value.

After adjusting and normalising the data for historical trades we achieve expected dollar performance at today’s market prices. The same trades would be made but after adjusting for a much higher current market price we get greater dollar values per trade. 

Process normalising past trades to current market price

1. Convert dollars to points

Each trades profit/loss dollar value will be converted to points for their respective markets e.g. $50 = 1 Point ES Market, $20 = 1 Point NQ, $5 = 1 Point YM

2. Convert points to percentage

Convert points to a percentage at that current time and market price e.g NQ trade in March 2007 (market trading at 1763.75 price) with $175.98 profitable trade = 8.8 Nasdaq points = 0.5% market move.

3. Normalise to market price

The 0.5% move is now normalised to this year's market price, NQ trading at 7711, this now equates to a $769.37 dollar value.

Normalising trades for expected dollar values

Drawdown metrics can be misleading when analysing system performance. Every investor wants to know what they can potentially lose or what a system has lost in the past, this way the investor has some expectation going forwards. This is a measure of risk and survivability, especially when you consider margins (futures are traded heavily on margin).

Drawdown percentage depends entirely on the starting capital. Most systems backtest reports are run with the assumption of starting $100k capital trading 1 contract with no compounding of returns. However, some developers will then inform investors that the system can be run with as little as $10k and still have the same performance.

Let’s assume the backtest report stated a 10% drawdown on $100k right of the start of trading = $10k drawdown.

Traders starting this with $35k the dollar value drawdown will be -$8211 trading 1 futures contract. Percentage drawdown is  23.46%. 

Futures are traded only in 1 contract increments. Markets move in percentages and price has risen remarkably over the last decade. It is better to normalise the trade data to the current market price to get a more accurate figure for a trades expected dollar value.

After adjusting and normalising the data for historical trades we achieve expected dollar performance at today’s market prices. The same trades would be made but after adjusting for a much higher current market price we get greater dollar values per trade. 

Process normalising past trades to current market price

1. Convert dollars to points

Each trades profit/loss dollar value will be converted to points for their respective markets e.g. $50 = 1 Point ES Market, $20 = 1 Point NQ, $5 = 1 Point YM

2. Convert points to percentage

Convert points to a percentage at that current time and market price e.g NQ trade in March 2007 (market trading at 1763.75 price) with $175.98 profitable trade = 8.8 Nasdaq points = 0.5% market move.

3. Normalise to market price

The 0.5% move is now normalised to this year's market price, NQ trading at 7711, this now equates to a $769.37 dollar value.

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