ETMA_The Evolution

Long time Harvesting pips with Zorro and Development is ongoing, new features are regularly implemented. Interesting Package, Their motto is " Take from the rich and give to the poor" Robin Hood style of Financial World .

Help Zorro to redistribute money http://www.zorro-trader.com/download.php

I forget to mention yesterday when i posted this that Zorro is Free Until 7000$ Profit, else u have to buy a Zorro S License which have a variable price depends on Supply and Demand .
Keep Withdrawing ur Money Every time u hit the 7000$ Profit Limit to Keep ur Free Zorro License Working, Cool .

Where and when reality hits the Fan : Seventeen Trade Methods That I Dont Really Understand , good luck buddy convincing others that this is the Hard truth, maybe a Gann Magic, an Elliot Wave or better yet an Astrology and Moon Cycle course will do the trick .

Over and over again i keep bumping at different research papers Suggesting to Scan the Market conditions before entering a trade to avoid unnecessary Drawdowns and keep ur profit Factor, ur expectancy and ur Sharpe and Sortino Ratio High .

Check out the Paper Link and the bottom of the Page for an Interesting articles too .

This is an Example of these research :

Testing Market Regime Indicators

Often when designing a system it's important to keep the big picture in mind. What is the overall market doing? The most simple way to accomplish this is to break the market into two regimes: bullish and bearish. We are all aware that price action is a mirror of human psychology of market participants, therefore price action can often be very different between these two regimes. Sudden market plunges that we see within strong bear markets, such as in 2008, behave much differently when contrasted to the continual grinding, upward market we saw after 2009. People behave differently under fear (how low can it go) and pain (look at all the money I lost) vs. doubt (this market can't go higher) and greed (I'll just make a bit more before I exit). Since people behave differently under these two market regimes it makes sense that we should design trading systems that take advantage of the different market characteristics for each regime. We want to build a system that dynamically adjusts its trading parameters based upon which market mode we're experiencing. To do this we can use any number of indicators.

The Indicators
The most simple way to divide the market is to use a 200-period simple moving average (SMA). When price is above it we are in a bull market regime. When price is below it we are in a bear market regime. Introducing this simple concept along can improve the performance of many trading systems. I've personally used this technique many times. Yet there are other techniques to divide a market and some of these might produce better results than our old reliable 200-day SMA. In this article I want to take a look at a few different methods.

The indicators we are going to test are:

1. SMA(200)
2. Rate of Change (ROC)
3. Smoothed Adaptive Momentum
4. Relative Strength Ranking (rsRank)

The Smoothed Adaptive Momentum and Relative Strength Ranking are two indicators you may not be aware of. First, the Smoothed Adaptive Momentum was created by John Ehlers. It's a complex indicator and there's more than I want to get into during this article. Google it if you wish and you can also find the EasyLanguage code here.

As for the RS Rank, you can find a little more about it here. I will say this about the rsRank for now, traditionally it's used as a ranking tool to compare a group of stocks or ETFs to determine which specific instrument is performing best. As its name implies, it ranks each instrument based on how well the instrument has been performing.
You can then compare this score to the other stocks or ETFs in your basket of trading instruments. Thus, you can simply pick the instrument with the highest rsRank score when creating a momentum-based trading system. In this article I'm using it for a completely different purpose and I was curious on how well it would hold up.

Smoothing The Indicators
All of these indicators are also smoothed to help reduce whipsaw. Of course the SMA is a smoothed price indicator so no modification was made to this indicator. Likewise the Smoothed Adaptive Momentum indicator by John Ehlers also contains a smoothing element already applied to the indicator. However, to smoothen the ROC and rsRank indicator I used the same technique found within the Ehlers indicator. It looks like this for the rsRank indicator:

rsRankSmoothed = ( rsRankValue + rsRankValue[1]*2 + rsRankValue[2]*2 + rsRankValue[3] )/6;

The same smoothing technique is applied to ROC as well.

Look-Back Period
We are going to use a 200-day period for all our examples. The 200 days represent about ten months of trading if you figure out that there are about 20 trading days per month. It's also a common longer term period applied to moving averages as well. This is not a magic number by any means. It's a large enough number that we should not see the regime switch too often between bear and bull. The idea is to capture the long term market mode, not every market gyration. Of course I encourage you to perform your own testing as well.

The Strategy Logic
The strategy code to test the effectiveness of these different indicators is rather simple. We are only going long during a bull market regime and closing our position when that bull market regime switches to a bear market regime. In essence we are creating a very simple trend following system. For each indicator the transition is based upon:

1. SMA: Bull market when price is above the SMA
2. ROC: Bull market when indicator value is above zero
3. Smoothed Adaptive Momentum: Bull market when indicator value is above zero
4. RS Ranking: Bull market when indicator value is above zero

Here is an example of the rsRank indicator used as a regime filter:
https://ci4.googleusercontent.com/pr...nk_Example.png
Long position opened when rsRank crosses above zero. Positions sold when rsRank crosses below zero.

Trading Environment
I coded the above rules in EasyLanguage and tested it on the SPY ETF market going back to 1993. All the tests within this article are going to use the following assumptions:

1. Starting account size of $50,000.
2. Dates tested are from 1983 through June 14, 2013.
3. The number of shares traded will be based on volatility estimation and risking no more than $2,000 per trade.
4. Volatility is estimated with a three times 10-day ATR calculation. This is done to normalize the amount of risk per trade.
5. The P&L is not accumulated to the starting equity.
6. There are no deductions for commissions and slippage.
7. A hard stop of $2,000 is placed for all trades.

SPY Results Since 1993
The following results are organized left-to-right by total return. We can see the ROC indicator has produced the best total return over the life of the study. It also produced the fewest trades and the highest number of winning trades. In a more distant second place is the rsRank indicator.

https://ci5.googleusercontent.com/pr...ators-1993.png

Notice the last row is the buy-and-hold results. None of the "systems" come close to B&H but please keep in mind these are not trading systems. We are testing indicators to see how they compare against one another in the task of picking out bull market regimes.

SPY Results Since 2000
When looking at the equity graph of the systems it becomes apparent that much of the gain occurred during the tech boom of the late 1990's. Since that crash, the market has behaved much differently. So I also tested the same set of indicators on the years after 2000. Those results are below:

https://ci6.googleusercontent.com/pr...ators-2000.png

Once again the results are organized left-to-right by total return. Our ROC rank has fallen to second place while SMA has moved into our number one position generating the highest return, but at what cost? We can see ROC continues to excel in terms of profit factor, the number of winning trades and reducing the number of trades. It holds up well on a number of factors that I like.

Conclusions
Based on these limited tests ROC shows superior results when determining a bull market from a bear market. In summary, put this to use in your own efforts at building a trading system. Use an indicator to determine the market mode and trade accordingly. The point here is to have your automated trading system automatically adapt to a changing market. For example, when the market is bullish you may want to look for longs only while ignoring shorting opportunities. Or if the market is within a bearish mode the criteria for entering a long position might be stricter than when in a bull market. Too often people simply trade the same setup or method during different market conditions. By breaking the market up into two regimes, you are making your trading system dynamic and adaptive to the changing market.

There are ways to segment a market further. Check out these articles as well.
Two Dimensional Market Filter
A Better Regime Filter To Boost Returns

See our website to download the strategy code used in this article.


System Trader Success
Powerful Ideas To Automate Your Profits
Copyright 2017. All Rights Reserved.

Connors 2-Period RSI Update for 2016

With the year 2016 well behind us, it's time to look at the very popular 2-period RSI trading method by Larry Connors and Cesar Alvarez. We all know there are no magic indicators but there is an indicator that certainly acted like magic over several decades. What indicator is it? Our reliable RSI indicator.
Over the past few years the standard 2-period trading model as defined in the book, "Short Term Trading Strategies That Work" has been making new equity highs.
Connors RSI Trading Model
The trading model as originally proposed by Larry Connors is very simple and it consist of long-only trades. As a reminder, the rules are as follows:

1. Price must be above its 200-day moving average.
2. Buy on close when cumulative RSI(2) is below 5.
3. Exit when price closes above the 5-day moving average.
4. There are no hard stops.

Testing Environment
Furthermore, I just want to state that all the tests within this article are going to use the following assumptions:

1. Starting Equity: $100,000.
2. Number of shares is normalized based on a 10-day ATR calculation with a $2,000 risk per trade.
3. There are no stops.
4. The P&L of each trade is not reinvested.
5. Commissions & Slippage are not accounted for.

During 2011 the market experienced a sudden and sustained drop which put the trading model into loss. Recall, the trading model had no stops. Since this drop the model has been slow and is now making new equity highs. Below is an equity graph depicting the trading model's equity curve trading the SPX index from 1980. You can easily see the large drop around trade number 135.

https://ci5.googleusercontent.com/pr...Graph-2016.png
https://ci5.googleusercontent.com/pr...16-433x600.png
Here is a closeup view of the last 28 trades, which covers about the last six years.
https://ci5.googleusercontent.com/pr...16-500x322.png

Below is the annual performance of this trading model over the past few years. We can see that years 2013 and 2014 in particular have seen a drastic reduction in the net profit. Is the strong bullish market which we have been experiencing over the past few years a temporary phenomena that is harming this trading model's performance? Could be. Or is simply this trading model slowly losing its edge? Yet, the last two years (2015 and 2016) have seen a return to more "normal" performance.

https://ci5.googleusercontent.com/pr...nnual-2016.png

Modified Trading Model
Back in the year 2013 I explored the robustness of the trading parameters used by the 2-period RSI trading model. That article can be found here. Within that article a slightly modified version of the original trading rules was proposed. In short, I doubled the value of the RSI threshold value (from 5 to 10) and doubled the look-back period for the simple moving average exit rule (from 5 to 10). Finally, a stop value of $2,000 was added. I picked this value because it represents our risk value when scaling the number of shares to trade. Notice we are only risking 2% of our $100,000 account on each trade. Here is a summary of the rule changes.

1. Use a value of 10 as the RSI threshold.
2. Use a 10-period simple moving average as our exit signal.
3. Use a $2,000 hard stop.

Below is a table showing the difference between the original Connors' rules and the modified Connors' rules.

https://ci6.googleusercontent.com/pr...mance-2016.png
Our increase in net profit comes at the cost of more trades which is due to the fact of lowering the stand on what we consider a viable pullback. By increasing the RSI threshold from 5 to 10 more setups qualify as a valid entry, thus we take more trades. We also increased our look-back period for our exit calculation. Thus, we should be holding some of the trades a little longer in an attempt to make more profit. The stop value does hurt the performance of our model. For example, removing the stop value will result in $181,620 in profit with a profit factor of 3.06. However, we're going to keep the stop in place because trading without a stop is something most people will not be doing! It will also help protect us from massive losing trades as seen in 2011.

Conclusion
The RSI indicator still appears to be a robust indicator at locating high probability entry points within the major market indices. You can modify the trigger threshold and holding period over a large range of values and still produce positive trading results. I hope this article will give you lots of ideas to explore on your own. Another idea with regards to testing parameters is to independently optimize the parameters over the "portfolio" of market ETFs instead of using just $SPX. There is no doubt in my mind the RSI indicator can be used as a basis for a profitable trading system.

TopTradersLive.com‏ @TopTradersLive
My upcoming podcast with Richard Dennis & 2 of his Turtles will kill a few myths! Watch this space @rjparkerjr09 @Covel @ritholtz @business

E-Ratio
March 9, 2017

Edge Ratio or E-Ratio measures how much a trade goes in your favor vs. how much a trade goes against you. The x-axis is the number of bars since the trading signal. A higher y-value signifies more "edge" at that step in time.

Measurements are normalized for volatility; this allows us to use e-ratio across all markets and regimes. Once normalized for volatility, 1 signifies that we have equal amounts of favorable movement compared to adverse movement.

In other words, the y-axis is an expression of how many units of volatility more or against you your trade gets. A measure of 1.2 would indicate .2 units more of favorable volatility and a measure of 0.8 would indicate .2 units more of adverse movement.

The blue line is for the selected strategy's signal and the red line is for a "random" strategy for the same market. The red line is to serve as a baseline to beat. Ideally, youll want to see a blue line above 1 and above the random line.

You may find many good strategies, but they may have an E-Ratio less than the red baseline or less than one. This would make us less confident that our signal will withstand the test of time.

Additionally, if E-Ratio falls off a cliff at bar 6 then it probably does not make sense to hold for 15 bars!

Another tool to make sure Build Alpha + Trader = Success.
How to calculate:

1. Record Maximum Adverse Excursion and Maximum Favorable Excursion at each time step since signal.
2. Normalize MAE and MFE for volatility. To compare across markets we need a common denominator. Lets use ATR or a unit of volatility.
3. Average all MFE and MAE values. Now you should have average MFE and average MAE at 1 bar since signal. Average MFE and average MAE at 2 bars since signal
4. Divide Average MFE by Average MAE at each time step.

Example. Calculate E-Ratio at one bar out from signal.

Signal 1:
MFE 1.50 ATR 1.27
MAE 1.00 ATR 1.27

Signal 2:
MFE 1.33 ATR 1.19
MAE 1.04 ATR 1.19

Signal 3:
MFE 1.83 ATR 1.67
MAE 1.27 ATR 1.67

Average MFE = ((1.50/1.27)+(1.33/1.19)+(1.83/1.67))/3 = 1.13
Average MAE = ((1.00/1.27)+(1.04/1.19)+(1.27/1.67))/3 = 0.81
E-Ratio at Bar One = 1.13/0.81 = 1.395

So in this example, one bar after our signal, we can expect ~.40 more units of volatility in our favor than against us. In other words, if ATR is 20 points then we can expect the trade to move on average 8 points (8/20 = .4) more in our favor than against us 1 bar after the signal is generated.

** Update **
I spoke quite a bit about E-Ratio or Edge Ratio in a recent podcast interview with Andrew over at BetterSystemTrader.com/79
Please check it out and let me know what you think. Thanks.

[email protected]
Copyright 2017 Build Alpha

BUILD ALPHA Alpha Spotter

What is Build Alpha?

Build Alpha is a genetic program that will search hundreds of thousands of possible entry signal combinations, exit criteria, and much more to form the best systematic trading strategies based on user selected fitness functions (Sharpe Ratio, Net Profit, etc.) and test criteria. Everything is point-and-click.

Shortly after hitting “Simulate”, the user will see hundreds of strategies in a sort-able table. The user will be able to view profit factors, Sharpe ratios, win percentages, equity curves, multiple different kinds of Monte Carlo analysis techniques, and our own unique forward Variance Testing to stress test each strategy.

Plus the ability to build portfolios of strategies, track correlations among strategies, stress test strategies, and much more. Additionally, Build Alpha will also print out full strategy code for NinjaTrader's C#, TradeStation's EasyLanguage, and MultiChart's PowerLanguage.

Another unique feature is the ability to compare signals and strategies vs. random signals and strategies. This provides the trader/money manager the ability to lower the probabilities that strategies discovered are possibly curve-fit and actually possess an edge more than simply random signals could have generated. In other words, the edge/strategy discovered has some predictive value that could not have been found by random chance. Build Alpha creates a random mined baseline each and every simulation - this serves as the threshold to beat (along with Buy and Hold return plots for every market).

079 – Strategy Validation with Dave Bergstrom

With the toolsets we have available to us today it’s really quite easy to create a trading strategy by just mining market data.
As we’ve just heard in that opening bit of audio and also from previous podcast guests too, if you try enough combinations you can find something that appears to work purely by chance or by luck.

The challenge however is trying to identify something that could be sustainable.
Something that may persist long enough in the future for us to take advantage of, and hopefully make some money from.

Our guest for this episode, Dave Bergstrom from BuildAlpha, has spent years researching, building, testing, and implementing market making and trading strategies for a high frequency trading firm, CTAs, money managers, individual clients, and even aspiring retail traders.

In this episode Dave is going to share some of his insights into strategy development and validation, including:

1. How adjusting the ratio of in-sample/out-of-sample data can lead to creating different types of strategies
2. Variance testing – what is it and how can it be used in the strategy creation process
3. How E-ratios can be used to determine how an edge decays over time & weed out potentially poor strategies with good backtest results
4. Why volume and volatility are important factors to consider when building trading strategies
5. Loads of other ideas to test and validate the robustness of trading strategies.

046 – Strategy Development with Perry Kaufman

I’m sure we all want to create trading strategies that perform better and last for longer but there are a number of issues we need to look out for when developing robust trading strategies, some are well-known and some perhaps aren’t.

In this episode we’ll be talking with Perry Kaufman about strategy development and more specifically some of the issues that can catch us out when creating trading strategies. Perry raises some interesting points about optimization that may not be well known plus he shares loads of tips to creating more robust strategies.

Perry writes extensively on markets and strategies, having published fourteen books and has just released a new book on building algorithmic trading strategies, which we’ll be discussing in this episode.

He has worked and consulted to a number of successful CTA, investment and prop trading groups, creating systematic trading and hedging programs.
This is also his 2nd appearance on the podcast, appearing as a guest way back in Episode 10.

Topics discussed

1. The most robust type of systems
2. How your choice of optimization values could be misrepresenting your results and how to choose parameters that give a more accurate picture
3. The mistakes traders make when analyzing optimization runs and tips to doing it properly
4. How to really determine if a new trading rule is robust
5. Reducing risk by using multiple parameters
6. What the number of profitable runs in an optimization can tell you about the robustness of a strategy
7. Why diversifying across strategies instead of across markets could be a better approach
8. The challenges of building robust strategies using Genetic Algorithms and Neural Networks

Top tips
Perry shared a number of great tips, here are some of my favorites:

1. Use average of results in an optimization run
2. Remove strategy rules that don’t add much value
3. Look at the % of profitable runs in a distribution, Perry personally likes to see 66% or higher runs to be profitable
4. When considering the values in an optimization range, look for the values to be evenly spaced %-wise, for example, 10/20/40/80, not linear like 10/20/30/40/50 because linear can make the larger values look more stable purely because they’re closer together as a %
5. When adjusting strategy rules, don’t be fooled by a few results going up, you want something that is generalized and improves most cases
6. Neural networks/genetic algorithms – feed in fewer items to get a more robust answer

All things Quant : Quant Mashup

Algorithmic trading: How to get started building an algorithmic trading system?

Late Night Post, something unusual is going on, take a look .

085 – Intraday Trading Strategies with Gary Hart

Hey Bass. You have been quiet of late. I hope all is well. :-)

Hey C., all is great, thank you for asking.

I was and still bored even thought i was researching extensively (Picture of my browser Included) as usual i didn't have the energy to share anything, beside being very busy learning to code MQL4-5, Easy Language and starting my first steps in Python - C# in my so little spare time .

Hope that everything is superb at your end .

Cheers.

All good my side Bass. That's some heavy duty learning you are doing. I am very envious. Finding quiet time to focus is getting harder and harder these days. Let me know how you go with Python and C#. It seems that I am a laggard in making the move and need some inspiration. Have a great week B :-)

Ah!, i forgot to tell you that i'm trying to acquire new habits, healthy ones !!!!.... out of boredom of course .
Get Refreshed: Drink More Water .

Crumbs. I hope that doesn't interfere with your regular cuppa at Starbucks.....getting healthy can kill you you know../tic :-)

Sacred, that's what Starbucks is for me, nothing can interfere with my Daily Lungo or Mocha, another Daily habit is to stash away the equivalent of 1 cup of Mocha / day which is $ 4.5 (tall Mocha) .

MARCH 2017 TREND FOLLOWING DOWN
April 5, 2017 by Jez Liberty

MARCH 2017 TREND FOLLOWING: DOWN -6.56% / YTD: -10.95%

More of the same for the State of Trend Following in March. The index continues its downward progress and established a new Max Drawdown figure last month.

https://www.elitetrader.com/et/threa...-wheel.148249/
https://www.quora.com/topic/Algorithmic-Trading

How can a beginner get started with quantitative trading?

Do forex robots work?
Laurent Bernut, ex-Fidelity short seller, ex-Hedge Funds analyst, ex-CPA, algo trader

Short answer: Yes. Now, if You think You can whip up a strategy over the week end and watch money roll in while You sip daiquiri by the pool, think again. Here is my story in Forex autotrade.

My story
In late 2003, after a short stint on the Sell side a strategist, i joined a start-up HF where i built the portfolio management system, trading system for multiple funds. I joined another one in 2005 because i wanted a more front office role, picking stocks. In 2007, i thought the game too easy and everything would end in tears, so i docked in Fidelity as a dedicated short seller. Tired of the boom and bust performance, i embarked on a programming journey. In August 2011, i started running a 100% systematic strategy developed on @wealthlabWealthLab (highly recommend the platform BTW). Strategy ran very profitably until the end of 2014, when i left Fidelity. In March 2015, i decided to re-write the strategy on Metatrader 4. Strategy has a proven track record, conversion from C# to C++, and the help of a senior programmer with the US Department of Defense, can’t go wrong, right?
So, we did and launched. We had such an Ackmanish performance that we pulled the plug after less than a month. Back to the drawing board for… 2 years.
I thought i had the right pedigree, but nothing prepared me for the challenge. It is by far the hardest endeavour of my career. It makes impossible problems such as understanding the opposite sex easy in comparison.
Forex has three distinctive features: 24/5 trading time with uneven volume, crushing leverage, shorter time frames.
These are the lessons i learned

1. Forex is a risk management exercise

The original strategy ran on equities daily bars with no leverage. Forex runs at higher frequency (30, 15 or 5mn). At daily bars level, the market has time to digest information. Signals are not so noisy. At higher frequency, the noise to signal ratio detetiorates significantly. Patterns do not form well.
With crushingly high leverage (100:1 and above), mistakes are costly. So, the game is to minimise drawdowns when strategy stops working and capitalise on the winners. The only way to do this is to develop an adaptive position sizing that will collapse risk in drawdowns and re-accelerate thereafter. We have developed a convex position sizing dll.

2. Time is the wrong container
Robots are designed to run on time charts. Now, volume is not constant throughout the day. Markets don’t trade much in the middle of the night but they are active between Europe and US open.
Our robot would detect valid signals during liquid hours and false positives during low volume hours. Time is the wrong container. BTW, this is the same reason flash crashes happen. Poorly designed algos treat low and high volumes uniformly.
Our solution has been to use constant volume charts. A big Thanks to Scott Phillips for introducing me to tick charts.

3. Forex is not a Christmas tree of indicators, moving averages, Fibo
Many traders use multiple indicators, oscillators. Charts end up looking like Christmas tree. This junk is redundant, fragile. Complexity is a form of laziness.
We use sophisticatedly simple rules that use onTick price only, no lagging derivative of it.

4. How to handle false positives and the science of stop loss
The shorter the periodicity, the noisier the market. Compounding effect of false positives + high frequency = rapid erosion of the equity curve. Stop losses are costly. So, the game is about avoiding stop losses and managing false positives.
These are thorny signal, stop loss and position handling issues. In fact, whatever the problem, better entry is rarely the right solution. We have developed three solutions:

1. scale out to reduce risk as early as possible. This comes from my experience as a short seller. First priority after entry is to reduce size/risk so as to mitigate upcoming short squeeze
2. French stop loss: markets are noisy. Patterns develop over time. So, we give enough wiggle room to each position to fully develop. stop loss is fashionably late, hence its name: French stop loss. The flip side is smaller position size
3. Multiple time frames: traders who look at multiple time frames want to use longer trends. We have found an inventive way to spot significant longer trends not by using different time frames, but point of significance within a single time frame
4. Time is a form of asset allocation: sometimes price goes sideways. Looking at different time frames, either short and/or longer, there are perfectly valid trends. So, the same strategy can be deployed across various time frames so as to hedge or supplement the pivot time frame

5. Philosophy, beliefs and concepts
At the end of the day, an automated strategy is the extreme formalisation of a philosophy. Complexity is a form of laziness. Every challenge required changing perspective, unlearning, simplification. This is the hardest and never ending part. Apple’s iOS will never be finished, and neither will our strategy.
In China, women usd to have their feet wrapped in banadage so as to make them petite. It is said that every centimeter was worth a barrel of tears. Every month on the strategy has brought its own barrel of tears.
Over the last two years, I have eaten enough humble pie to open an international bakery chain. It has been a long painful journey of unlearning and purification of concepts to their essence. If i had known beforehand what i was up for, i would have looked for a job in the HF world. This is much more cushy than designing a Forex robot.

Conclusion
Going in, i thought the strategy i used to run was sophisticated. Now in retrospect, it seems crude, blunt disrespectful of the markets. You must respect the markets for one reason only, because markets certainly will not respect You back.
Forex robots work, but they are incredibly hard to design and complicated to program bug-free.
Ironically, it is easy to develop the skills to assess robustness of autotrade strategies. Rather than trying to program your own robot, i would focus on analysis existing ones. Calculate a weekly (if available) monthly:

1. Common Sense Ratio to assess robustness amidst drawdowns
2. Performance/Ulcer Index to determine the quality of performance
3. ask about position sizing and Voila

Now, the above paragraphs may discourage any aspiring strategist. Here is the greatest lesson of all: building an automated strategy is like building a timepiece. Until the last cog fits in, time will be off. It will be one hell of a tourbillon but persevere, don’t give up. You might be one cog away from sipping daiquiris by the pool and watch the money roll in.

The problem with profitable trading is that u have to unlearn all that u learned during your early Journey in trading in order to let new sound ideas to get in, good luck doing that.

Cheers,
bass