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How I applied Buffet's strategies to my own portfolio, +70% networth, beat SP500 by 40%

I believe I did pretty well in the market this year. My networth increased ~65% since its lowest point in March, ~350k to 620k. 20k from the car I bought in March. I rolled over a 401k and it messed up Mint's reporting, hence the spike from Jul -> Aug.
I beat the SP500 by 40% in my YOLO account, my FAANG account went from 180->300
I did this by following some basic investing principles, buying and holding for the most part, being patient, and only investing in areas which I have expertise in.
I did not buy into the TSLA hype, nor do I play options, nor do I play crypto.

High level advice:

https://www.simplysafedividends.com/intelligent-income/posts/37-top-10-pieces-of-investment-advice-from-warren-buffett
I picked the 7 I agree with.
  1. Invest in what you know…and nothing more.
  2. Never compromise on business quality
  3. When you buy a stock, plan to hold it forever
  4. Diversification can be dangerous
  5. Most news is noise, not news (don't read articles about investing)
  6. The best moves are usually boring (buy and hold)
  7. Only listen to those you know and trust
I firmly believe that anyone who follows those concepts, they will find success in investing.

General mindset:

Application:

I was very specific in the types of companies I would choose to invest in within tech. I decided to follow my strengths. As a data engineer, I'm very intimate with cloud technologies, and I think I generally have pretty sharp business acumen and good strategic direction.
As a result, my day to day work had me using a ton of technologies in the cloud space. I've used Splunk, NewRelic, Twilio, AWS, GCP, Hortonworks/Cloudera, Oracle, Tableau, Datadog, Sendgrid (bought by Twilio), Dropbox/box, Slack, Salesforce, Marketo, Databricks, Snowflake, HP Vertica, just to name a few. I was familiar with CDN services like Fastly and Cloudflare because sometimes, I worked with the DevOps and IT guys.
Based on industry hearsay, day to day work, eventually, I got a good "feel" of what technologies were widely adopted, easy to use, and had a good reputation in the industry. Similarly, I also got a feel for what tech were being considered 'dated' or not widely used (HP, Oracle, Cloudera, Dropbox, Box).
I tend to shy away from companies that I don't understand. In the past, most times I've done that-- I got burned. My biggest losers this year was betting on $NAT and $JMNA (10k total loss). After learning from those mistakes, I decided to only focus on investing in companies that either I or my peers have intimate first hand experience with using. Because of this rationale, the majority of stocks in my portfolio are products which I believe in, I thoroughly enjoy using, and I would recommend to my friends, family, and colleagues.
Post COVID, due to the shift to remote work and increase in online shopping I decided to double down on tech. I already knew that eCommerce was the next big thing. I made very early investments into SHOP and Amazon in 2017 for that reason.
My hypothesis was that post-COVID, the shift on increased online activity, remote work, and eCommerce would mean that companies which build tools to support increased online activity should also increase. I decided to choose three sectors within tech to narrow down-- these were three sectors that I had a good understanding of, due to the nature of my work and personal habits.
  1. eCommerce + AdTech
  2. IT/DevOps (increased online activity means higher need for infra)
  3. FinTech (increased shopping activity means more transactions)
These are the points I consider before I consider jumping into a stock:
  1. Do I feel good about using the company? Do I believe in the company's vision?
  2. Where do I see this company in 5 years? 10 years? Do I see my potential children being around to use these companies?
  3. What does YoY, QoQ growth look like for this company?
  4. Is/Will this product be a core part of how businesses or people operate?
  5. Who are their customers and target demographic?
  6. (SaaS) Customer testimonials, white papers, case studies. If it's for a technology, I'm going to want to read a paper or use case.
In March, I took what I believe to be an "educated gamble". When the market crashed, I liquefied most of my non tech assets and reinvested them into tech. Some of the holdings I already had, some holdings were newly purchased.
EDIT ^ this isn't called timing the market you /wsb imbeciles. Timing the market would be trying to figure out when to PULL OUT during ATH and then buying the dip. I SOLD at the lowest point, and I with the cash I sold AT A LOSS, I reinvested that cash and doubled down into tech. If I sold in Feb, and bought back in March, that would be calling timing the market. What I am doing is called REINVESTING/REBALANCING... not timing the market.
I have 50% of my networth in AMZN, MSFT, AAPL, GOOG, FB, NFLX, and the rest in individual securities/mutual funds. I have 3 shares of TSLA that I got in @1.5.
Here are the non FAANGs I chose.
  1. $SQ. I had already been invested in SQ since 2016. I made several bad trades, holding when it first blew past 90 until I sold it at 70... bought in again last year at 60s, after noticing that more and more B&M stores were getting rid of their clunky POS systems and replacing it with Square's physical readers. After COVID, I noticed a lot of pop up vendors, restaurants doing take out. A Square reader made transactions very easy to make post-COVID.
  2. $ATVI. Call of Duty and Candy Crush print money for them. I've been a Blizzard fanboy since I was a kid, so I have to keep this just out of principle.
  3. $SHOP. They turned a profit this year, and I think there is still a lot more room to grow. It's become somewhat of a household name. I've met quite a few people who mentioned that they have a Shopify site set up to do their side hustle. I've tried the product myself, and can definitely attest that it's pretty easy to get an online shop up and running within a day. I 5.5xed my return here.
  4. $BIGC. I bought into this shortly after IPO. I'm very excited to see an American Shopify. BigC focuses on enterprise customers right now, and Shopify independent merchants, so I don't see them directly competing. I'm self aware this is essentially a gamble. I got in at 90, sold at 140, and added more in 120s. I def got lucky here... it's not common for IPOs to pop so suddenly. I honestly wasn't expecting it to pop so soon.
  5. $OKTA. Best in class SSO tool. Amazing tool that keeps tracks of all of my sign-ons at work.
  6. $DDOG. Great monitoring tool. Widely adopted and good recommendations throughout the industry. Always had a nice looking booth at GoogleNext.
  7. $ZM. Zoom was the only video conf tool at work which I had a good time using. Adoption had blown up pre-COVID already in the tech world, and post-COVID, they somehow became a noun. "Zoom parties" and "Zoom dates" somehow became a thing interwoven into peoples' day to day lives.
  8. $TWLO. Twilio sells APIs which allow applications to send messages like text, voice, and video chat. For example, when DoorDash sends you a text at 1 AM reminding you that your bad decision has arrived, that text is powered by Twilio. In March, New York announced that they were going to use Twilio to send SMS notifs for COVID contact tracing.
  9. $NET/$FSTY. These two two seem like the ones best poised for growth in the CDN space. This is based off of industry exposure and chatting with people who work in DevOps.
  10. $DOCU. people aren't going to office to sign stuff, super easy to use, I like their product.
  11. $WMT. eComm, streaming, and a very substantial engineering investment makes me think they have room to grow. Also I really need to diversify.
  12. $COST. When is the last time you heard someone say "Man I hate going to Costco and paying $1.50 for a hotdog and soda?" Diversification. Also cheap hotdogs.
  13. $NVDA/AMD. GPUs are the present and the future. Not only are they used for video games, but Machine Learning now uses GPU instead of CPU to do compute (Tensorflow for example). Crypto is still a thing as well, and there will always been a constant need for GPUs.
Mutual funds/ETFs 1. $FSCSX. MF which focuses on FinTech.
  1. $VTSAX Pretty much moves with the SP500.
  2. $WCLD. Holdings include Salesforce, Workday, Zuora, Atlassian, Okta, New Relic, Fastly...
Titanvest: I was an early access user, and I was able to secure 0% fees for my accout. 36% gains so far. I like them, because their portfolio happens to include shares of tech giants that I either don't have individual stocks for or my stake is low (CRM, PPYL). It nicely complements my existing portfolio.

Some things I do that that are against the grain:

One example was how I applied the above principle was to WalMart. In 2018 I noticed that I was getting targeted by a lot of Data engineering job listing for WalMartLabs-- WarMart's tech division. The role was to build out a big data pipeline to support their eCommerce platform. WalMart's online store released in Q3 of 2019. Post COVID, I used their online store and it was a seamless experience. They even offer a 5% cash back card like Amazon. They reported strong Q4 sales last year, and they did very well post COVID. Why did I choose to invest in $WMT? Because I believe that Wal-Mart has room to grow for their online platform.
Lastly... remember that wealth isn't accrued over time. It takes years to build. The quickest way to increase your wealth is by investing in yourself-- your career and earning potential. The sooner my income increased, the quicker I had more capital to buy into stocks.
Also, if you've gotten this far, the point of my post isn't to say that you should invest into tech. The message I'm trying to get across is-- when picking companies, pick companies in fields or verticals you have good knowledge in. Heed Buffet's advice to only pick companies you believe in and understand. Play to your strengths, don't mindless toss money based on one person's posts on Reddit-- always do your own due diligence. Use DD as a guide and use personal research and experience to drive your decision.
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Former investment bank FX trader: Risk management part 3/3

Former investment bank FX trader: Risk management part 3/3
Welcome to the third and final part of this chapter.
Thank you all for the 100s of comments and upvotes - maybe this post will take us above 1,000 for this topic!
Keep any feedback or questions coming in the replies below.
Before you read this note, please start with Part I and then Part II so it hangs together and makes sense.
Part III
  • Squeezes and other risks
  • Market positioning
  • Bet correlation
  • Crap trades, timeouts and monthly limits

Squeezes and other risks

We are going to cover three common risks that traders face: events; squeezes, asymmetric bets.

Events

Economic releases can cause large short-term volatility. The most famous is Non Farm Payrolls, which is the most widely watched measure of US employment levels and affects the price of many instruments.On an NFP announcement currencies like EURUSD might jump (or drop) 100 pips no problem.
This is fine and there are trading strategies that one may employ around this but the key thing is to be aware of these releases.You can find economic calendars all over the internet - including on this site - and you need only check if there are any major releases each day or week.
For example, if you are trading off some intraday chart and scalping a few pips here and there it would be highly sensible to go into a known data release flat as it is pure coin-toss and not the reason for your trading. It only takes five minutes each day to plan for the day ahead so do not get caught out by this. Many retail traders get stopped out on such events when price volatility is at its peak.

Squeezes

Short squeezes bring a lot of danger and perhaps some opportunity.
The story of VW and Porsche is the best short squeeze ever. Throughout these articles we've used FX examples wherever possible but in this one instance the concept (which is also highly relevant in FX) is best illustrated with an historical lesson from a different asset class.
A short squeeze is when a participant ends up in a short position they are forced to cover. Especially when the rest of the market knows that this participant can be bullied into stopping out at terrible levels, provided the market can briefly drive the price into their pain zone.

There's a reason for the car, don't worry
Hedge funds had been shorting VW stock. However the amount of VW stock available to buy in the open market was actually quite limited. The local government owned a chunk and Porsche itself had bought and locked away around 30%. Neither of these would sell to the hedge-funds so a good amount of the stock was un-buyable at any price.
If you sell or short a stock you must be prepared to buy it back to go flat at some point.
To cut a long story short, Porsche bought a lot of call options on VW stock. These options gave them the right to purchase VW stock from banks at slightly above market price.
Eventually the banks who had sold these options realised there was no VW stock to go out and buy since the German government wouldn’t sell its allocation and Porsche wouldn’t either. If Porsche called in the options the banks were in trouble.
Porsche called in the options which forced the shorts to buy stock - at whatever price they could get it.
The price squeezed higher as those that were short got massively squeezed and stopped out. For one brief moment in 2008, VW was the world’s most valuable company. Shorts were burned hard.

Incredible event
Porsche apparently made $11.5 billion on the trade. The BBC described Porsche as “a hedge fund with a carmaker attached.”
If this all seems exotic then know that the same thing happens in FX all the time. If everyone in the market is talking about a key level in EURUSD being 1.2050 then you can bet the market will try to push through 1.2050 just to take out any short stops at that level. Whether it then rallies higher or fails and trades back lower is a different matter entirely.
This brings us on to the matter of crowded trades. We will look at positioning in more detail in the next section. Crowded trades are dangerous for PNL. If everyone believes EURUSD is going down and has already sold EURUSD then you run the risk of a short squeeze.
For additional selling to take place you need a very good reason for people to add to their position whereas a move in the other direction could force mass buying to cover their shorts.
A trading mentor when I worked at the investment bank once advised me:
Always think about which move would cause the maximum people the maximum pain. That move is precisely what you should be watching out for at all times.

Asymmetric losses

Also known as picking up pennies in front of a steamroller. This risk has caught out many a retail trader. Sometimes it is referred to as a "negative skew" strategy.
Ideally what you are looking for is asymmetric risk trade set-ups: that is where the downside is clearly defined and smaller than the upside. What you want to avoid is the opposite.
A famous example of this going wrong was the Swiss National Bank de-peg in 2012.
The Swiss National Bank had said they would defend the price of EURCHF so that it did not go below 1.2. Many people believed it could never go below 1.2 due to this. Many retail traders therefore opted for a strategy that some describe as ‘picking up pennies in front of a steam-roller’.
They would would buy EURCHF above the peg level and hope for a tiny rally of several pips before selling them back and keep doing this repeatedly. Often they were highly leveraged at 100:1 so that they could amplify the profit of the tiny 5-10 pip rally.
Then this happened.

Something that changed FX markets forever
The SNB suddenly did the unthinkable. They stopped defending the price. CHF jumped and so EURCHF (the number of CHF per 1 EUR) dropped to new lows very fast. Clearly, this trade had horrific risk : reward asymmetry: you risked 30% to make 0.05%.
Other strategies like naively selling options have the same result. You win a small amount of money each day and then spectacularly blow up at some point down the line.

Market positioning

We have talked about short squeezes. But how do you know what the market position is? And should you care?
Let’s start with the first. You should definitely care.
Let’s imagine the entire market is exceptionally long EURUSD and positioning reaches extreme levels. This makes EURUSD very vulnerable.
To keep the price going higher EURUSD needs to attract fresh buy orders. If everyone is already long and has no room to add, what can incentivise people to keep buying? The news flow might be good. They may believe EURUSD goes higher. But they have already bought and have their maximum position on.
On the flip side, if there’s an unexpected event and EURUSD gaps lower you will have the entire market trying to exit the position at the same time. Like a herd of cows running through a single doorway. Messy.
We are going to look at this in more detail in a later chapter, where we discuss ‘carry’ trades. For now this TRYJPY chart might provide some idea of what a rush to the exits of a crowded position looks like.

A carry trade position clear-out in action
Knowing if the market is currently at extreme levels of long or short can therefore be helpful.
The CFTC makes available a weekly report, which details the overall positions of speculative traders “Non Commercial Traders” in some of the major futures products. This includes futures tied to deliverable FX pairs such as EURUSD as well as products such as gold. The report is called “CFTC Commitments of Traders” ("COT").
This is a great benchmark. It is far more representative of the overall market than the proprietary ones offered by retail brokers as it covers a far larger cross-section of the institutional market.
Generally market participants will not pay a lot of attention to commercial hedgers, which are also detailed in the report. This data is worth tracking but these folks are simply hedging real-world transactions rather than speculating so their activity is far less revealing and far more noisy.
You can find the data online for free and download it directly here.

Raw format is kinda hard to work with

However, many websites will chart this for you free of charge and you may find it more convenient to look at it that way. Just google “CFTC positioning charts”.

But you can easily get visualisations
You can visually spot extreme positioning. It is extremely powerful.
Bear in mind the reports come out Friday afternoon US time and the report is a snapshot up to the prior Tuesday. That means it is a lagged report - by the time it is released it is a few days out of date. For longer term trades where you hold positions for weeks this is of course still pretty helpful information.
As well as the absolute level (is the speculative market net long or short) you can also use this to pick up on changes in positioning.
For example if bad news comes out how much does the net short increase? If good news comes out, the market may remain net short but how much did they buy back?
A lot of traders ask themselves “Does the market have this trade on?” The positioning data is a good method for answering this. It provides a good finger on the pulse of the wider market sentiment and activity.
For example you might say: “There was lots of noise about the good employment numbers in the US. However, there wasn’t actually a lot of position change on the back of it. Maybe everyone who wants to buy already has. What would happen now if bad news came out?”
In general traders will be wary of entering a crowded position because it will be hard to attract additional buyers or sellers and there could be an aggressive exit.
If you want to enter a trade that is showing extreme levels of positioning you must think carefully about this dynamic.

Bet correlation

Retail traders often drastically underestimate how correlated their bets are.
Through bitter experience, I have learned that a mistake in position correlation is the root of some of the most serious problems in trading. If you have eight highly correlated positions, then you are really trading one position that is eight times as large.
Bruce Kovner of hedge fund, Caxton Associates
For example, if you are trading a bunch of pairs against the USD you will end up with a simply huge USD exposure. A single USD-trigger can ruin all your bets. Your ideal scenario — and it isn’t always possible — would be to have a highly diversified portfolio of bets that do not move in tandem.
Look at this chart. Inverted USD index (DXY) is green. AUDUSD is orange. EURUSD is blue.

Chart from TradingView
So the whole thing is just one big USD trade! If you are long AUDUSD, long EURUSD, and short DXY you have three anti USD bets that are all likely to work or fail together.
The more diversified your portfolio of bets are, the more risk you can take on each.
There’s a really good video, explaining the benefits of diversification from Ray Dalio.
A systematic fund with access to an investable universe of 10,000 instruments has more opportunity to make a better risk-adjusted return than a trader who only focuses on three symbols. Diversification really is the closest thing to a free lunch in finance.
But let’s be pragmatic and realistic. Human retail traders don’t have capacity to run even one hundred bets at a time. More realistic would be an average of 2-3 trades on simultaneously. So what can be done?
For example:
  • You might diversify across time horizons by having a mix of short-term and long-term trades.
  • You might diversify across asset classes - trading some FX but also crypto and equities.
  • You might diversify your trade generation approach so you are not relying on the same indicators or drivers on each trade.
  • You might diversify your exposure to the market regime by having some trades that assume a trend will continue (momentum) and some that assume we will be range-bound (carry).
And so on. Basically you want to scan your portfolio of trades and make sure you are not putting all your eggs in one basket. If some trades underperform others will perform - assuming the bets are not correlated - and that way you can ensure your overall portfolio takes less risk per unit of return.
The key thing is to start thinking about a portfolio of bets and what each new trade offers to your existing portfolio of risk. Will it diversify or amplify a current exposure?

Crap trades, timeouts and monthly limits

One common mistake is to get bored and restless and put on crap trades. This just means trades in which you have low conviction.
It is perfectly fine not to trade. If you feel like you do not understand the market at a particular point, simply choose not to trade.
Flat is a position.
Do not waste your bullets on rubbish trades. Only enter a trade when you have carefully considered it from all angles and feel good about the risk. This will make it far easier to hold onto the trade if it moves against you at any point. You actually believe in it.
Equally, you need to set monthly limits. A standard limit might be a 10% account balance stop per month. At that point you close all your positions immediately and stop trading till next month.

Be strict with yourself and walk away
Let’s assume you started the year with $100k and made 5% in January so enter Feb with $105k balance. Your stop is therefore 10% of $105k or $10.5k . If your account balance dips to $94.5k ($105k-$10.5k) then you stop yourself out and don’t resume trading till March the first.
Having monthly calendar breaks is nice for another reason. Say you made a load of money in January. You don’t want to start February feeling you are up 5% or it is too tempting to avoid trading all month and protect the existing win. Each month and each year should feel like a clean slate and an independent period.
Everyone has trading slumps. It is perfectly normal. It will definitely happen to you at some stage. The trick is to take a break and refocus. Conserve your capital by not trading a lot whilst you are on a losing streak. This period will be much harder for you emotionally and you’ll end up making suboptimal decisions. An enforced break will help you see the bigger picture.
Put in place a process before you start trading and then it’ll be easy to follow and will feel much less emotional. Remember: the market doesn’t care if you win or lose, it is nothing personal.
When your head has cooled and you feel calm you return the next month and begin the task of building back your account balance.

That's a wrap on risk management

Thanks for taking time to read this three-part chapter on risk management. I hope you enjoyed it. Do comment in the replies if you have any questions or feedback.
Remember: the most important part of trading is not making money. It is not losing money. Always start with that principle. I hope these three notes have provided some food for thought on how you might approach risk management and are of practical use to you when trading. Avoiding mistakes is not a sexy tagline but it is an effective and reliable way to improve results.
Next up I will be writing about an exciting topic I think many traders should look at rather differently: news trading. Please follow on here to receive notifications and the broad outline is below.
News Trading Part I
  • Introduction
  • Why use the economic calendar
  • Reading the economic calendar
  • Knowing what's priced in
  • Surveys
  • Interest rates
  • First order thinking vs second order thinking
News Trading Part II
  • Preparing for quantitative and qualitative releases
  • Data surprise index
  • Using recent events to predict future reactions
  • Buy the rumour, sell the fact
  • The mysterious 'position trim' effect
  • Reversals
  • Some key FX releases
***

Disclaimer:This content is not investment advice and you should not place any reliance on it. The views expressed are the author's own and should not be attributed to any other person, including their employer.
submitted by getmrmarket to Forex [link] [comments]

The 4th way of algorithmic trading (Signal Processing)

Algorithmic trading types classified based on development perspectives:
1) Technical Analysis
2) Statistics and Probability
3) Machine Learning
I took a different path which is not discussed widely in this subreddit.
4) Signal Processing
I'm not a good storyteller, but this is my journey and advices for the beginners
First, my background:
- Electrical and Electronic engineer,
- Software developer (20+ years)
- Trader (5+ years)
- Algorithmic trader (3+ years)

How I Found The Alpha:

Before algorithmic trading, I was somehow profitable tradeinvestor. Like most of you, when I began to algorithmic trading, I tried to find magic combination of technical indicators and parameters. Also I threw OHLCV and indicators data into the RNN for prediction.
I saw that, even very simple strategies like famous moving average crossover is profitable under right market conditions with correct parameters. But you must watch it carefully and if you fell it is not working anymore, you must shut it down. It means you must be experienced trader to take care of your algorithm.
I am a fulltime software developer, algorithmic trading was my side project also it became my hobby. I tried to learn everything about this industry. I watched and listened hundreds of hours of podcasts and videos in all my free time like commuting from home to work.
These are the most useful to me:
- Chat with traders: https://www.youtube.com/channel/UCdnzT5Tl6pAkATOiDsPhqcg
- Top traders unplugged: https://www.youtube.com/usetoptraderslive
- Ukspreadbetting: https://www.youtube.com/channel/UCnKPQUoCRb1Vu-qWwWituGQ
Also I read plenty of academic papers, blog posts and this subreddit for inspiration.
Inspiration came from my field, electronics. I will not give you much detail about it but I have developed a novel signal processing technique. It is a fast and natural technique which I couldn’t find any article or paper which mention this method. It can transform any interval price data into meaningful, tradable form. The best part is, it doesn't require any parameter and it adapts to changing market conditions intrinsically.
These are the concepts that inspire me:
- Information Theory: https://en.wikipedia.org/wiki/Information_theory
- Signal Processing: https://en.wikipedia.org/wiki/Signal_processing
- ADC: https://en.wikipedia.org/wiki/Analog-to-digital_converter

What a Coincidence:

While googling to improve my algorithm, I found out that, Signal Processing is used by Jim Simon's Renaissance Technologies according to various sources including wikipedia: https://en.wikipedia.org/wiki/Financial_signal_processing

Proverbs Integration:

Output of the process can be used to develop endless type of profitable strategies. I made some money with different momentum based strategies while thinking about how I can use this technique more efficiently.
I like to combine different fields. I think trading and life itself have many things in common. So beside general trading concepts, I think that I can try to implement concepts of the life. Also because of the parameterless design, it's more like a decision making process than an optimization problem.
I searched proverbs and advices for better decision making. I handled them one by one and thought how I could implement them in a unified strategy while preserving the parameterless design. In time, this process was significantly improved stability and reliability while it was evolving from momentum to mean reversion.
These are some proverbs which I use them at various aspects of the algorithm:

- “The bamboo that bends is stronger than the oak that resists.” (Japanese proverb)
- "When the rainwater rises and descends down to where you want to cross, wait until it settles." (Sun-Tzu)
- "If you do not expect the unexpected you will not find it, for it is not to be reached by search or trail" (Heraclitus)
If you wonder how I implement them in the code, think about the last one; how do you define the unexpected, how to wait for it and how to prepare your algorithm to generate profit.
By the way, I strongly recommend: The Art of War (Sun-Tzu)

Result:

I have plenty of ideas waiting to be tested and problems that need to be solved. Nevertheless these are the some of the backtest results, for the time being:
Crypto:
- Market fee and spread are considered, slippage is not.
- For multiple assets testing; Survivorship bias was attempted to be eliminated using historical market rank of the assets. Data is acquired from coinmarketcap.com weekly report.

ETH / BTC
BNB / BTC
Binance Historical Top 100 / BTC
Other Markets:
My main focus is crypto trading. But all the improvements are cross checked in different markets and intervals and validated empirically and logically. It can’t beat every asset and every interval but it tends to work profitably across them.

https://preview.redd.it/l865fw6mjfd51.png?width=900&format=png&auto=webp&s=ff217d35637b41e26db8d7cfc3df14c3fb7ec14e
Live:
The algorithm is running live for over 1.5 years with evolving strategies I mention before. The last one is running for months.

Warnings and Advices:

- Bugs: A few months ago, before bedtime, I released new version for fixing small cosmetic bug and gone to sleep. When I woke up, I saw that nearly 40% of my account wiped out in a few hours. Instead of live settings, I published test settings. It was very painful. I have been coding since childhood, so everyone must be careful. I recommend, implement hard limit for stopping the algorithm.
- Fully Automatic Strategy: Finding an edge is not enough. If you need fully automated trading system, you need a portfolio manager (a lot of research is going on at this field) and especially an asset selector mechanism which is maybe more important than the edge itself. If your algorithm is not be able to select which assets to trade, you must select manually. It's not an easy task and it's prone to error. I was very lucky with that: A mechanism already contained in the algorithm was used to rank and select the assets based on their momentums.
- Fee-Spread: Because of the market fee and spread, trading is a negative sum game. Do not ignore it when backtesting your algorithm.
- Slippage: It's really a problem for low volume assets like penny stocks and lower market cap crypto currencies. Stay away from them or play with small capital or find a way to determine how much money you can use.
- Latency: Don’t think it's a HFT only problem. If your algorithm synchronize multiple assets data from the market and run calculations before sending order back to the market, you lose significant amount of time. This usually causes losses that you have not considered before, especially in a volatile environment. Also if you want to develop realtime strategy, you must seriously consider what you will do in downtime.
- Datasource: This is the most important part for preparation before developing you strategy. If you don’t have good, reliable data; you cannot develop a good strategy. For free data for various market; I suggest investing.com, but you should consider that volume data is not provided. For crypto, all of the exchanges provide their real data for any asset and any interval, you can use them freely. Also you can buy data , especially if you want intraday data, but I can't suggest any because I never tested them.
- Biases: Before developing algorithm, please take a look at and understand the common biases like: Survivorship bias, Look-ahead bias, Time period bias. Or you can be sure that you will face them when you go live.
- Live trading: When you think your algorithm can make money, don’t wait till perfection. Go live as soon as possible with small capital to wake up from your dreams and face with the facts early.
- Psychology: If your education is based on STEM and you don’t have trading experience, it’s not easy in the real world to swallow all those ups and downs that you see in minutes during backtest. It can affect your mood and your life much more than you think. I suggest, work with a professional trader or only invest what you can really afford to lose.

Last Words:

After over 3 years of journey, I have a profitable algorithm that I trust. I was supposed to lie on the beach and drink beer while my algorithm printing money. But I am consistently checking it’s health and I have always things to do like all software development projects.
I posted some of the backtest results, but I don’t know are they considered P/L Porn or not. If so, I can remove it.
Sorry about mysterious parts of this post. I removed some parts unwillingly before posting, but there is really a thin line between giving away your edge freely (also it means loosing it) and inspiring people to find their own way.

“Non est ad astra mollis e terris via" - Seneca

EDIT:


For those engineers and EE students who are bombing my inbox for guessing what I did; I can not write all of you in private, also I want to explain it publicly.
I must say, you are on the wrong way. If I open sourced the signal processing part, probably it doesnt mean anything to you and you can not turn it into a profitable algorithm.
I have to clarify that; before I developed the technique, I knew what I am looking for exactly. Signal processing is not magically trading the market, I am trading the market. it's just a tool to do what is in my mind near perfectly.
Also proverbs are the way of thinking. I read them and think if it means anything for trading.

Lastly watch the Kung Fu Panda :)
https://www.youtube.com/watch?v=rHvCQEr_ETk

submitted by if-not-null to algotrading [link] [comments]

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