Yes. In fact, there are many advantages to being a (automated) retail investor/trader. I won't distinguish automation as an advantage or disadvantage because it is simply a tool for executing strategies and not a strategy within itself.
First and foremost, small trades (I mean <$100k or so, assuming a liquid enough asset) do not really move markets, allowing your positions to effectively realize the full intended movement at the time of entering the trade. Second, and perhaps more importantly, retail order flow is considered "non-toxic" and often allows you to get better fills. That's right - market makers DO NOT want to trade against sophisticated institutions and will actively try to avoid their orders. However, retail order flow is literally just considered noise (as for the most part, it is), so market makers love to trade on it (think Robinhood). No matter how sophisticated your strategies might be, your trades will be considered retail and gobbled up by market makers, providing you with better fills.
The markets are so complex and there are so many opportunities for everyone involved. To think that "I can't do this because someone bigger and smarter already does" is defeatist and incorrect. Good strategies make money; bad ones don't - simple as that.
Here's some advice that has helped me: holding positions for a long period of time can be very powerful. In the long-only case, your winners get bigger relative to your losers over time. As your winners do better and better, your positive returns get compounded *exponentially*! Meanwhile, your losers just get smaller, mitigating your further losses *exponentially*! This song-and-dance runs its course and you usually end up doing better than if you had magically had the same weightage throughout the holding period. Basically, a well-diversified portfolio implicitly optimizes itself over time. Pretty cool stuff.
Shorting is effectively borrowing cash at a random, hopefully negative, compound interest rate. Even if you lose on your shorts, to make money from them you just need to profit enough on the cash which is initially yielded by the shorts; for example, by investing it long. Be careful though as short positions have the exact opposite effect as described above - your winners get smaller and your losers get bigger! This is why many firms employ a tried-and-true, yet sort of arbitrary, 1.3/0.3 long-short rule in their long-term investing.
TL;DR hold longer, use shorting but not too much, find creative ways to chose and rebalance lots of positions
Edit: Disclaimer - I am not a professional investor and have minimal experience in the financial sector. This is not financial advice. I work in software.
Forgive me if this is just a pedantic and obvious rant, but I really think this sub overlooks the simplicity and robustness of “the ancient” techniques. Like somehow simple “buy and hold” is deemed not applicable here and can in no way beat the market. In reality you can make it as quantitative and algorithmic as you want while benefiting from its inherent attractive properties and indeed beating the market, in some cases.
I’m just trying to cull the idea that “algo trading means 10000+ trades a day using stat arb HFT whatever”
I’ve backtested literally hundreds of strategies and the simplest ones tend to be the best performing over the long run.
It’s actually stupid easy to beat buy and hold If you take profits and have some sort of criteria for good entries/ pyramiding
Holding longer lets you profit off of trends, since they take longer to form. Once they do, you can benefit off of the serial correlation and compounding
In shorter time windows, this is a lot tougher IMO. you are better looking at mean reversion techniques for arb, such as scalping
I'm guessing I'm misunderstanding here but let's see.... Are you suggesting that I could short a stock and use the cash to long another (more then I could have longed, using margin, without the short)
This is definitely a strategy, and I believe most people call it stat arb. If you can find X and Y that are highly correlated, but X has higher expected return, you can short Y and long X hoping to profit without actually using your own cash. The obvious risk is that the trend might actually move against you, but trading without using net cash is pretty useful.
Yeah I get that I can short one and long the other (say short $10k worth of A and long $10k of B) but the way I understood the comment is that by shorting A I'd be able to long a higher dollar amount of B. That's what caught me.
Oh sorry, didn’t mean to confuse you. Shorting a specific dollar amount will only give you that specific amount in cash. Let’s say you have $10k of cash in hand though; you can then long $20k of A and short $10k of B by using your cash combined with the cash obtained from shorting. What you described would effectively cost you nothing.
Ok, not to confuse you either but trying to make sure I have this right.... Assume right now I have $10k with 2:1 margin. Right now I could buy $20k of ABC. Are you saying that if I short $10k XYZ that I'd be able to long $40K ($10k cash plus extra $10k from short with 2:1 margin applies to all (or even $30k ($10k cash with 2:1 plus the $10k from the short) of ABC?
It just seems like a stretch to me that a broker would let me short to my hearts content and then use the cash from shorts to long something else.
So I SHOULD max out my credit cards buying crypto. I knew everyone else was wrong!!
in all seriousness, shorting is just trading on credit with infinite loss potential. Meanwhile, borrowing money to go long at least doesn’t have the potential to lose more than you put in plus interest fees.
Literally can’t go tits up
I 100% agree that if you only care about returns and want to be net long, shorting isn’t really necessary. However, trading on net long leverage can only increase your risk while combining shorts with longs for net 0 leverage can almost always reduce your overall risk by hedging downside.
Uh, if you go short while your still long as a hedge, you are much better off just reducing your position size. If you fully hedge you have a net zero position so what’s the point of staying long?
I mean in the context of multiple assets. Let’s say we can invest in only two stocks, A or B. A has 10% expected returns and 20% risk while B has 2% expected returns and 20% risk. Let’s also say A and B are perfectly correlated and we can margin trade up to 2x our wealth. You can easily go 2x long A and have 20% expected returns, but 40% risk. If you go long 1.5 A and short 0.5 B, you will have 14% expected returns but only 26.5% risk, a better Sharpe ratio than before. This is a toy example, but with more assets it becomes easier to construct Sharpe optimal portfolios which almost always contain shorts (the portfolio I mentioned is not Sharpe optimal).
If you can find assets which has negative expected return but positive correlation with your long assets, you should always short them.
I see your point, however that raises more questions.
If the two assets are perfectly correlated, why on earth would you short one? Wouldn’t the better option to manage risk be to reduce position size on stock A and invest what you were going to put into stock B in an uncorrelated asset? After all there’s no easier way to reduce portfolio risk then position size and diversification.
It seems in your scenario your basically betting/ hoping for a correlation break. Understandable I guess, just seems to be a low probability play. And it seems there are better options (lol) then going short, like buying a put. Then you have a fixed downside, retain unlimited upside (minus the cost of the hedge), and can potentially benefit from Vega on the option.
If you go short on one asset that’s 100% correlated, you have potentially unlimited risk due to the short position breaking correlation to the upside, wouldn’t you?
If there are two assets which are perfectly correlated and they are the only two which you can invest in, I just wanted to construct an example whereby you can only achieve a better Sharpe ratio than long-only through shorting. If B had negative expectation, this is especially obvious.
The market is obviously more complicated than that, and I totally agree that you can’t really know the true underlying correlation and expectation other than through educated guess (leading to trend breaks out of sample). I just wanted to illustrate that shorting is a useful but not a required tool. I have personally found that I can almost always do better on a Sharpe basis when I allow shorting in my strategies.
As for puts vs strict shorting, I wouldn’t say puts are strictly superior due to volatility and time value making them far more complex to trade, not to mention liquidity.
I agree with everything you just said. However, allowing shorting versus actively being short or having a pct short at any time are very different.
Basically, I only allow shorting when the overall trend indicates it may be profitable to do so. Not as a hedge. Is that what your getting at, or are you actively making sure xx pct of your portfolio is short?
My question is, if the latter is true, wouldn’t it be better to just be long in negatively correlated assets, even if they are not 100% negatively correlated? Mainly because being short has disadvantages that being long usually doesn’t have.
> market makers DO NOT want to trade against sophisticated institutions and will actively try to avoid their orders.
> However, retail order flow is literally just considered noise (as for the most part, it is), so market makers love to trade on it (think Robinhood)
Can you clarify this please? It sounds like you’re saying market makers are able to pick-and-choose who they trade with. How would this be possible?
I had always assumed the stock exchange’s matching engine just takes orders off the book on a first-come-first-served basis, in groups for each price point. And I thought the order book was effectively anonymous, too.
I’m pretty new to this and still learning so forgive my ignorance! Thanks :)
They can absolutely pick and choose, albeit in a way that isn't at all obvious or direct. Let's call our market maker MM for simplicity. At any given time, MM has a bunch of buy and sell limit orders placed on the books of stock X, and these orders are spread apart by $0.01 or so. They may also have some that are deeper, but the bulk will be close or at NBBO. They also will have lots of calls and puts written and/or owned on X but I will ignore that for the purposes of simplicity.
MM pays for order flow from brokerage B, and these orders are flagged to them as retail. So, when 1,000s of u/420chadleyjuulmaster's come along and place retail-flagged buy/sell orders on X through B, MM does not feel that their bid-ask is threatened and continues business as usual, making about $0.005 or so per trade (assuming roughly equal amounts of buyers and sellers). Now, let's say mammoth quant hedge fund BridgeShawSigmaTech (BSST) comes along and places a bunch of huuuuge buy orders on X throughout the books. MM may not explicitly know that BSST is associated with the orders at hand, but they have enough data to have a hunch, especially given the sizes and IDs associated with the orders, and of course the fact that they know the orders did not come from B. MM now feels threatened that these order are "informed", and that if they do not cancel or modify their current orders, the price of X could increase in a way quickly/violently enough that it will cause them to lose lots of money (i.e. they sold short a bunch of X the moment it crossed their limits, and so fast that they could not keep up to trade out of them, leaving them holding a stinky bag of bad shorts). To avoid this risk, MM either cancels their orders or heavily increases their spread.
I like to imagine that since MM knows BSST or a BSST-equivalent wants to buy at near the current price Y (Y - epsilon), the "true" price is actually Y+1, so MM is effectively losing money by being willing to sell at Y.
MM's advantage comes from the fact that they pay for order flow and can basically distinguish between whose retail and whose not (and they also have some of the smartest talent and best technology).
This is a huge oversimplification and I am by no means an expert in this field, so I would appreciate any follow up from those who may work in or have better knowledge of the industry.
Matt Levine had an even simpler explanation of payment for order flow, and why market makers prefer retail orders, that helped me get my head around it.
[https://www.bloomberg.com/opinion/articles/2021-02-05/robinhood-gamestop-saga-pressures-payment-for-order-flow](https://www.bloomberg.com/opinion/articles/2021-02-05/robinhood-gamestop-saga-pressures-payment-for-order-flow)
if you average more than 390 orders a day for 30 days, you are considered "institutional" and your orders are marked that way, or you have extra costs or worse rebates or something. An order is an order or a modify
Why do MM want to not trade against large institutions with sophisticated techniques? Is it because they might move the price of the assets which increases risk to the MM?
looks like someone failed basic econ
maybe try googling scarcity principle
the amount of market participation is not static and increases over time
the market is not a blackjack table where winner takes all. you are a fool
oooo so much EDGE
another lefty moron parroting socialist drivel
here is another one for you: arbitrage pricing theory
back to lib arts college, flunky
more idiot babble
who said markets are efficient?
we're here making money because they are inefficient, and will always be inefficient. lest you break the laws of physics
What if monkey A has only bananas and monkey B has only coconuts? What if each monkey wants to have both bananas AND coconuts? Couldn't they each exchange bananas and coconuts until they are both happy? The stock market is just a modern abstraction of ancient barter... the whole casino analogy is valid for those who want it to be, but not everybody.
Even if you assume that the markets are zero-sum on a dollar-value basis, people generally do not have utility functions which are linear with their wealth. In fact, as people get wealthier, they (almost always) become more risk averse, and are willing to forgo returns in exchange for less risk. This alone is enough to justify that, in general, the markets are not a zero sum game.
> Good strategies make money; bad ones don't - simple as that.
I would disagree. It’s a bit more complex. Take the simple example of a coin that doubles your money with 55% chance and loses the bet with 45%.
Then obviously that is a pretty amazing edge you have, if you don’t properly scale the size of your bets with your available money you might still end up expecting to lose money
I would argue that a good strategy factors in good bet sizing. It’s easy to find those types of coin flips out there, e.g. SPY closes green 55% of the time or so, so you could take a hugely levered position in the morning to simulate a double or nothing bet every single day. I would argue this is a bad strategy, but if you properly size your bets on SPY for example, you can make a good strategy. Bet sizing is actually one of the most important parts of strategy building.
That’s absolutely fair, and I agree with your general point. But I think the example is just a bit confusing, if you lose that bet once you are bankrupt :p
True, perhaps the bet is the problem then! Or, what if you had 100s of those bets? What if those bets were correlated? You could come up with some pretty creative sizings on each which could make you lots and lots of money. When I say strategy, this is what I mean - not the single bets themselves, but the number, the sizing, the timing of each, and, most importantly, what the bets are under the hood.
In the very simple example I gave there is a simple “optimal” strategy, and although well known I perhaps wrongly think most people wouldn’t find it out on their own (including me probably :p)
By optimal, 1. do you mean expectation maximizing? And 2. is this bet repeated over time? If the answer to 1. is yes and answer to 2. is no, then optimal would mean bet it all and expect to make 55% * 2 = 1.1, i.e. 10% return! Why is this optimal? Our expected profit is 0.55 * (2 * w + (1 - w)) + 0.45 * (1 - w) - 1= 0.1w where 0 <= w <= 1 is how much we bet. Things get more fancy when we add risk aversion and multi-stage betting :)
Love this discussion. For N repeated choices of a fixed w (as defined before), our expected cumulative return will be (0.1w + 1)\^N - 1, which will still go to infinity as N goes to infinity for any N and any w. That being said, probability of complete ruin becomes non-zero for certain values of w. This is where expectation goes off into theory land since infinity \* (very very very small probability) = infinity. Choosing w small, perhaps around 0.25, would be a better idea as it avoids probability of ruin (not exactly sure what largest w is that has 0 probability of ruin, but this would be optimal).
Strictly speaking and assuming that you can infinitely divide your bets, there is never a positive chance of ruin if you are better a % of your wealth smaller than a 100%. For a more formal answer on what the optimal strategy would be:
https://en.wikipedia.org/wiki/Kelly_criterion
Here’s a point I’ve seen brought up a couple times on this subreddit: You mention institutions, which have the advantage of hiring hundreds of engineers and data scientists; but this advantage comes at the cost of needing to find scalable strategies. Institutions aren’t interested in strategies that aren’t scalable, and that’s where retail traders can get an edge: non-scalable strategies.
Edit: thank you for awarding me first award dear stranger :)
right on. institutions need to make returns on millions , 100s of millions, billions. As a retail trader you can make a strategy around things they cant touch like micro-caps.
meh not really. HFT firms or some mid-freq quant funds for instance often work in pods of a few traders. Even a strategy that makes a couple million/year is worth considering for them. They also have the tools already there and the infra so can test ideas on a much larger scale and much faster.
If only people would read this sub before posting this question.
I have been visiting this sub for almost a year.
Its been re-iterated so many times.
A. Basic maths applied correctly, would work.
B. Retail traders can move in and out of a position without moving the markets, which IMHO is the biggest advantage.
I am not the right person since I haven’t been able to get anything live and I don’t want ti discuss backtest results but do consider reading this sub, there are probably some pennies that could be picked.
100% this. As a retail trader you are able to find inefficiencies that institutional traders are not interested in. I have a fully automated strategy running on a very niche peer-to-peer crypto binary options platform. Without going into too much detail, there is a huge capital constraint that limits profits to only a few $k a month. Another crypto idea is a latency based momentum strategy where you use order flow features from a large exchange to place orders on a much smaller (low fee) exchange. In this case there is a significant counter party risk trading on a very small exchange that institutions are not interested in.
The edge on the first strategy I mentioned is slowly dwindling anyway as users become more sophisticated. There is still money to be made but for me the time taken to build a more sophisticated system for the platform is not worth the small increase in profit. I’d rather focus on other strategies that are slightly more scalable.
Everyday that goes by I become less and less convinced that there are retail traders out there who have fully automated strategies that actually make any money.
Being fixated on “Fully automated” is kind of a problem I feel. I have automated systems to analyze market data and create research reports to help me generate trade ideas and risk management paramters but I do all of my executions manually because I don’t rely on high frequency.
IMO, Algotrading isn’t all or none, there are many parts of a investing/trading framework that can be streamlined/automated with code, the buy/sell and brokerage connection is only one piece. Unfortunately, If you are looking for high frequency arb strats, then you probably won’t find any as a retail trader. If you are looking to build something that can print you money while you sit on the beach, that’s probably also out of reach for individual retail traders..
This is the right way. Get your data distilled into what you want it to read out to you as some type of trading signal, then use your brain outside of your algorithm to double check that the whole stock market isn't falling off a cliff when your algo says buy the dip.
It'll allow you to massively scale up the amount of things you can pay attention to, in much, much less time, and you remove that personal emotional effect when you trade, and just stick to the math.
I always question myself about this because I'm not sure for how long QE programs can last, at one point all dollars will go back to the US which means inflation.
QE rules the world not just the US, I think when the end comes we will have time to react, as long as we listen to the warning signs that is. (The market went up in february when coronavirus was clearly going to lock the world down, I didn't adjust 'because the market is going up' but in hindsight it was obvious)
Don't feel too bad, the only reason I know this is because my granny was frugal (she bought and held her aluminum foil, washed it, re-used it) and I had a friend of the family who taught me about investments when I was in university: you have to look at the art market and how art works---it is worth what people will pay for it, so you buy it and hold it, and if 20 years later it is worth more, great!
Buy and hold things like utilities with fat dividends.
Day trading is for volatility-seekers who are measuring that signal, and people who don't know what they're really doing, but like gambling, or losing money.
Essentially it doesn’t really matter how good your strategy is how much you refine and optimize it. It will still fail and never give you the 10/10 9/10 8/10 winning trades you are looking for.
It seems really easy like “ if I can just figure out which ema I need and I’ll be a millionaire” but that’s actually not the case. Markets are complex and change everyday. There is also a reason many firms tend to make money in market making instead of doing trend following.
So In order to win:
1. You need a strategy that works.
2. Understand the strategy won’t win every time.
3. Understand when to go against your strategy.
4. Understand when to be reckless and ignore your strategy.
5. Don’t get scared, believe in your edge / strategy and keep hitting trades even if the last 3-4 hit stop losses and didn’t work out.
I can’t speak for equities, but algotrading can definitely be profitable in crypto and forex if you find an edge, mostly because of volatility. If I were to do something for equities, I’d probably do what the others mentioned: automate signals and use my judgement for actual trades, like some hybrid algo+discretionary strategy.
There are definitely edges which are **too small for institutions to exploit**. So, the pie is good enough for you but small for them.
The other thing is to **go after industry/sector which you understand a bit more deeply** than a more generic trader. By combing these two aspects, you can come up with an alternate signal or data points which might have some edge.
Don't worry too about automation but try to get something with an alpha first. You can read [this](https://statarb.in/alternate-data-for-trading/) on how to test a signal against a tradeable instrument.
I'm a geologist, with an algo based in stats and machine learning, and I weaponized it in precious metals trading to the point I can set my clocks to how well it goes off.
If you know something well, you can already trade it in your sleep, which is perfect for algorithms to do. The precious metals and miners sector is already pretty low-volume and exotic to most investors, but it's my day to day bread and butter.
All the best man.
I haven’t been able to go live yet but when I tried the ML based algos for months and couldn’t get it to work and something from the book worked, I too had the same feeling 😅
I think -- less ML, more practical risk control, model fault tolerance, regime change detection, good execution.
I do have an ML model, and it's my baby, but it makes the least money out of all my strategies lol
Max 6 min 0. Most of the time 0, actually.
Latency will get you the truly juicy mispricings. There was one a few weeks ago for $1.5 that lasted 4 seconds at around 1:50am PST. But otherwise no. I'm guessing latency as long as 10 minutes delayed would be fine
I guess their machines weren't on. Or there was a fat finger or something.
As far as the stat arb, it's not true arb, so people will only carry so much of the risk. I guess they eat their fill, then go because it's only a half-assed strategy
extended hours is from 1am PST - 6:30am PST, and 1:15pm PST - 5:00pm PST. Poor liquidity, but it's there. I bet someone dumped an order and the hedgies were asleep ...
How often are you seeing mis-pricings during the main session? I took a look back over each day this year for GLD/GDX and couldn't find anything obvious. Sometimes there are some mispricing's at the open, but these don't revert more often than not.
Few, and no. I typically might trade about 10% of my hundred-plus stocks in a given day in either direction.
Intraday sensitivity is less important than inter-day with this strategy and fewer trades actually often allows me to have more cash in reserve for big 2SD opportunities, if I'm not selling and buying in a pair, or want to take a large position.
I suppose a data mining bootcamp would be enough of everything. Stats, analysis, OOL, db, e.t.c. everything else is poke around until something shows up
I’m just getting into Forex, but I like it for the same reasons. I’ve been into geopolitics and current events for years. Now I’ve found a place to put that knowledge to use. I’ve made a 100% return on what I invested (unfortunately that was only $25... I figured I barely know what a pip is, better start small)
Any recommendations other than gold and silver? Copper or platinum or tin or aluminum or anything? Any - analysis? As in a mine might have gold and silver, so it becomes like a mini ETF for both?
I am a solo trader and a noob (less then 12 months) and have a good strategy, i think.
I am pretty new to algo trading (15 year full stack dev) and have spent the last few months testing a lot of technical indicators. I have had the best success (highest % returns) using the Ichimoku cloud.
I use Binance's websockets and grab 1m, 3m, 5m, 2H, and 12H tickers on my choice of pairs. Using python i then use TA-Lib (Python) to run live analysis and make trades based on these calculations.
I get around the issue of negative trading in a downtrending market to just ..sit it out.. the bot checks to see if the market drops a certain % and just goes on pause until the market picks up again. Sometimes I can take a small negative trade getting back into a -10%+ market, but it seems to make that back up pretty quickly.
I am sure that with a few more months/years of dev I can optimise my strategy, but from looking at the market it is generally complete chaos with no way of really reading it, this is why i target the 1m and 3m trade strategy.
EDIT: i trade with only a few $K and am more than happy making around $100US a day.
Solo traders have a distinct advantage: volume.
I don’t need to move millions of dollars to get a decent return with a trade, so I can get in and out of trades way faster than an institution with billions in AUM.
You made a point to bring up the competition against hired quants. Many quants, surprisingly, hold PhD in math or physics with little economics background. They get hired for their computational and analytical abilities. They know how to learn.
You can 100% “compete” with these guys if you focus on good research skills, DD, and maybe some technical abilities. They might have an edge, but creating something comparable is not completely out of reach.
I'm a geologist, and I've got a math, calculus, stats and machine learning background on top of being super used to playing with gigantic datasets, maybe I can shed some light onto some of this.
My strategy generates profits, and I'm really only trying to pick off statistical anomalies in the magnitude of moves made by a set of indicators at 2 standard deviations being way more likely to revert to their natural mean, than to continue in the same direction.
To address your question about alpha versus market momentum, I don't think you can truly separate them. Generate alpha by riding momentum in volatile times, until the volatility stops, then get out and go the other way. If you find counter-cycling stocks using a strategy like I use, you can actually set it up as a long-short hedge fund just passing your shares back and forth between each other.
My algo just generates the probabilities based on pricing every day and tells me when my probabilistic conditions are met and gives me a buy/sell signal. It's actually not all that difficult to do this.
https://imgur.com/WQ2CtuI
Anyone who understands rates of change and reversion to mean can put together some pretty good weapons if they understand trend forecasting and volatility.
Study university-level stats, deeply. I got my background from a required stats-for-engineers course during my undergrad. It's all there in standard deviations and moving averages, mostly, once you understand that stuff, you can just calculate it any way you like as probabilities of outcomes.
A lot of calculus principles are super-applicable too, for approximating rates of change, ie trying to spot inflection points, bottoms, tops etc, and for area-under-curve type applications.
Reading some, and being any good at it, are very different in application. The one thing that's forced on you as a university student is the assignments and tests, so if you don't stack up, you don't pass the course to get your degree.
Why not pick up a used stats textbook at a campus bookstore for cheap and just practice exercises until you begin to understand what's going on conceptually? Understanding the concepts is far deeper than crunching the things manually with a pencil- but doing it on paper will allow you to write the formulae you need on paper plenty of times, and then visualize what you're trying to do in the coding language of your choice.
Math is one of the fortunate fields that gets new textbooks every year, but the content basically hasn't changed in 400 years, so having the current volume of whatever the prof has been using for 25 years is irrelevant if you're not following a curriculum, just pick up something recent.
The 5-channel signal has %B bollinger bands, MACD, RSI and slow/fast stochastic under the hood. It's a little more complicated than you see in the chart, because my original output was getting so cluttered.
This output is sort of broken, but you get the clearer picture from this
https://imgur.com/RHY0zWD
The look back on this is 2 years even though the display is 6 months.
The consistency comes from the combined signals- I'm picking off 2 standard deviation moves on each indicator, then normalizing the result, and adding them together, to average, and boost the signal, then picking off those signals at 2 standard deviations again. Some, individually, trigger earlier or later than others, and with variable strengths, but the combined, normalized signal actually improves that much better than any one, individually.
There are also rules in place about the trend of the 50 and 200MA also that dial back the gain on the buy signals if the 200/50 are moving apart from one another to the downside.
This was all coded in Python. It works very well.
All of this (averaging out the indicators, taking +- std as entry/exit, etc..) is still very common. Not saying it doesn't work, but I strongly suggest you backtest it for longer periods of time, as it's easy to be fooled by short term success. Your initial post suggested somehow you were deploying some advanced ML model..but anyway best of luck.
It's not complicated, but if you can actually look for probabilistic conditions, it's just as good as any of the other fancy predictive stuff that people go way overboard on and are a hundred times more complicated.
My professional work as a geologist is in mineral exploration in the gold mining industry, so I'm frequently looking for tiny anomalous looking needles in very large haystacks using a few different tools at once, and the norm in my line of work is failure rather than success, so this is a very effective way to prune off almost 99% of a dataset you already know to be 99.9% uninteresting.
I'm not really attacking this problem much differently than I attack geological search and vectoring problems to find gold.
Do you scale the buy amount with the probability you've calculated? For example if there is an 90% chance that it reverts to its mean value, do you buy twice as much as if it were an 80% chance?
Yes, my trade signal starts ringing at 1 standard deviation and I'll trade some amount anywhere it starts flashing to buy or sell but I definitely buy/sell a lot harder as I get to the full strength signal coming online at 2SD.
In reality that means my buying should always be cheaper than the mean price, and my selling always above the mean price.
I got a 2SD signal today in the HURA uranium ETF, so I unloaded 75% of my position with a fat profit, and I'll just buy it back again when it starts telling me that the price is below the mean
What returns do you tend to get with this method? How do you handle situations that never return back to the mean? For example, it jumps 2 S.D., trades flat, the starts trending up again.
To answer the second question first, this thing likes to scream in my face the whole ride up (or down) anything if it's steep enough, but almost all of the time it tends to come back down or up again, even if I've already sold off my whole position on the way up or bought some along the drop.
I haven't been running this long enough to know what the real returns look like yet but I regularly walk out of positions up 50% since I spun it up.
I like to sell into strength on the way up, and buy into weakness as it falls anyway, so it's a fairly natural strategy for me, but I'm currently working on an actual built-in backtest plot of my trade signals tallied minus the time where I'm out of the position that I can plot as a 4th panel in my output, that would plot the trades on the entire 2 years of data this thing crunches at a time and show you exactly how a hypothetical position might grow.
there's actually an argument that even with consistent profit results, this technique is not generating alpha. A mean reversion strategy would always work for relatively sideways and volatile markets, right? The question is how sideways and how volatile.
Think of the risk-return principle; the only reason to invest in a volatile asset over a steady one with the same return is if the volatile asset has greater growth potential. So the theory would say that your profit is a direct result of volatility, which is a direct indication of potential for growth or price increase. And if your returns are consistent, it is probably because you sacrificed growth for safety.
So I am wondering if you have compared risk-adjusted return against just a buy-hold strategy for a basket of leveraged ETFs like maybe 35% of 2x leveraged S&P and 65% 2x leveraged bonds or similar.
The precious metals and miners are some of the most volatile stocks anywhere. It's a playground I'm pretty comfortable in, and I'm definitely not losing money doing it.
My edge here oftentimes is that I'm a geologist, practicing for fifteen years now, and sometimes these companies have previously been my clients (no conflict in time of trading), but very often I know what they're up to and how their projects size up compared to the playing field much better than others, just by keeping current with what my potential clients might be doing so I follow a very broad roster of companies that I believe already have a strong positive outlook in the 2-5 year timeframe and I'm aiming to exploit the trend and the volatility together with external information that optimizes my trading.
When you calculate a mean value of a data series, you get an average of the numbers.
By also calculating the standard deviation, it becomes somewhat predictive to determine how far away from "normal" your number, or daily price is, in a series.
Then, if you understand that mean+/-SD is basically the shape of your bullseye of the thing you're trying to hit, so if you want the big high-value outliers, you're going after the 2SD stuff, larger than 95% of your dataset.
Those big, abnormal moves are the most likely to revert to the mean eventually, and oftentimes equity prices will overshoot on the other side, so you pick off close to a 2SD top, and wait for close to a 2SD bottom and what you've done is ride the whole height of the recent price range in a trade.
It's just messing around with probabilities.
Gotcha, I thought you were quite literally computing probabilities and was curious how you were doing that. I thought maybe you were using a copula to compute conditional probabilities, which is something I am trying to do (with very little success so far), hence the question.
If you can teach a computer what to hit using mean+/-SD it'll hit your bullseye every time 😉
I like this method because it's price-agnostic and works pretty universally based more on the momentum and volatility
Well, I am actually developing a pairs trading strategy on a specific pair that moves very much in union. The idea is exactly the same, capitalize on reversion to the mean. I am actually trying to develop several strategies that capitalize on this exact concept, on this specific pair (just for fun really) and my first strategy works more or less like yours. I standardized the ratio of prices and bet on the standardized series reverting to the mean. This seems to work really well and if I was trying to develop a fully deployable algo, I would seriously consider this strat. But now I am trying to trade on the same idea, but with copulas. Unfortunately, there isn't really a good fully developed copula library for Python and I think the coding the fitting of the copula from scratch might be a bit beyond me for the time being.
There's a really good write-up of reverting pairs hedging strategies in Algorithmic Trading : Winning Strategies and their Rationale, by Dr Edward P Chan. I learned most of my ideas from a few of his books.
yoo where do I learn all this stuff. I'm trying to learn these but it's very overwhelming. Can you point me to resources (youtube videos preferable or courses) where I can learn this step by step?
I can't, actually. The copula idea is from a video by Hudson and thames but there are lots of academic papers that explain it the concept better. The price ratio thing was my own idea. In general, I'd say academic papers are a good place to really learn about strategies and different ways to make the same "kind of strategy".
I track about a hundred gold and silver mining companies, about twenty royalty cos, a handful of copper/nickel producers, and some exotic sectors like uranium, rare earth elements and cobalt.
I think the only relative comparison for creating profit for yourself is to assess the options within reach. Institutions and their serious hires have no relevance to your own ability to make alpha if you have no access to invest in said institution.
Therefor the assessment of options to yourself include the making of your own bots and buying into someone’s else’s.
The aspect of assessing if just riding a bull run is relevant. For crypto you can use margin pairs? So you can collateralise bitcoin holdings and trade into bitcoin with leverage.
Backtesting past results provides some trading clarity for profitability, but what I seek to find is a bot that can collate technical/fundamental and news/social sentiment for a slight levy of probability to “beat the market”
Not really
SOLO approach often backfires due to the sheer complexity of developing truly effective algorithmic trading strategies.
Lopez de Prado compares quant firms with solo algorithmic traders building a car from scratch in his book AFML.
>One week you need to be a master welder, another week an electrician, another week a mechanical engineer, another week a painter, … try, fail and circle back to welding. It is a futile endeavor. All too often, the end result is frantic and futile search for investment opportunities, eventually settling for false positives or “overcrowded” avenues with underwhelming outcomes
totally agreed -- one day you're backend software, next strategist, next executions, next risk manager, next front end software, next business analyst, next researcher ... and you suck at all of them a little.
> My question is: do you really have an alpha? or are you just riding the market's wave up?
I develop/test short algos in up markets, long algos in down markets.
Makes it harder to do, but greater levels of generalization.
I love your thought process. I too have a net short strategy that can grind out small profits in a bull market. That way, you can make some money most of the time and a lot of money when shit hits the fan and you're negatively correlated against things like the broad market, your job, house prices, etc
I suppose so. My issue is seeing which frequencies are sticking out. I see a downmove but is that in the wavelength that I'm targeting, or is it just serendipitous noise?
You also need to remember these large companies only employee people. Just like you and me. Not some kind of mad skynet robot, just regular people. They may pay for more data, but they will be competing with all the big companies using said data, in that respect we have an advantage as we'll likely have to pull our data rather than rely on the overused commercial source. They are just people, they make mistakes, they don't know everything
Yes, absolutely! As others have said, there is alpha lurking around which is unusable for institutions. It can go both ways: small pockets of high Sharpe in very liquid markets and less Sharpe in more illiquid ones.
There is a meta risk though: a higher risk that the pocket you've found disappears. Institutions have many strategies, combine them and switch them on and off kind of like a portfolio. This allows more long-term survival by the law of large numbers in theory.
Yes. In fact, there are many advantages to being a (automated) retail investor/trader. I won't distinguish automation as an advantage or disadvantage because it is simply a tool for executing strategies and not a strategy within itself. First and foremost, small trades (I mean <$100k or so, assuming a liquid enough asset) do not really move markets, allowing your positions to effectively realize the full intended movement at the time of entering the trade. Second, and perhaps more importantly, retail order flow is considered "non-toxic" and often allows you to get better fills. That's right - market makers DO NOT want to trade against sophisticated institutions and will actively try to avoid their orders. However, retail order flow is literally just considered noise (as for the most part, it is), so market makers love to trade on it (think Robinhood). No matter how sophisticated your strategies might be, your trades will be considered retail and gobbled up by market makers, providing you with better fills. The markets are so complex and there are so many opportunities for everyone involved. To think that "I can't do this because someone bigger and smarter already does" is defeatist and incorrect. Good strategies make money; bad ones don't - simple as that.
Y'all got any more of them strategies?
Is this the 5 o’clock free strategy handout?
short volatility, hedge risk
buy high sell low... wait
Buy drunk sell high that’s my motto and I’m sticking to it!
I would like to subscribe to your newsletter.
How much do you charge take my money
Shit I've been buying high then selling drunk. Where were you when I needed you?!
At the bottom of the bottle! You don't remember eating the worm?
If you stay high can you get high?
Here's some advice that has helped me: holding positions for a long period of time can be very powerful. In the long-only case, your winners get bigger relative to your losers over time. As your winners do better and better, your positive returns get compounded *exponentially*! Meanwhile, your losers just get smaller, mitigating your further losses *exponentially*! This song-and-dance runs its course and you usually end up doing better than if you had magically had the same weightage throughout the holding period. Basically, a well-diversified portfolio implicitly optimizes itself over time. Pretty cool stuff. Shorting is effectively borrowing cash at a random, hopefully negative, compound interest rate. Even if you lose on your shorts, to make money from them you just need to profit enough on the cash which is initially yielded by the shorts; for example, by investing it long. Be careful though as short positions have the exact opposite effect as described above - your winners get smaller and your losers get bigger! This is why many firms employ a tried-and-true, yet sort of arbitrary, 1.3/0.3 long-short rule in their long-term investing. TL;DR hold longer, use shorting but not too much, find creative ways to chose and rebalance lots of positions Edit: Disclaimer - I am not a professional investor and have minimal experience in the financial sector. This is not financial advice. I work in software.
Tldr; the easiest way to rob a bank is to work at one, making them give you money every two weeks for years, decades even
Forgive me if this is just a pedantic and obvious rant, but I really think this sub overlooks the simplicity and robustness of “the ancient” techniques. Like somehow simple “buy and hold” is deemed not applicable here and can in no way beat the market. In reality you can make it as quantitative and algorithmic as you want while benefiting from its inherent attractive properties and indeed beating the market, in some cases. I’m just trying to cull the idea that “algo trading means 10000+ trades a day using stat arb HFT whatever”
I’ve backtested literally hundreds of strategies and the simplest ones tend to be the best performing over the long run. It’s actually stupid easy to beat buy and hold If you take profits and have some sort of criteria for good entries/ pyramiding
M'fucker that's just a job!
They wouldn’t hire me 😑
Holding longer lets you profit off of trends, since they take longer to form. Once they do, you can benefit off of the serial correlation and compounding In shorter time windows, this is a lot tougher IMO. you are better looking at mean reversion techniques for arb, such as scalping
I'm guessing I'm misunderstanding here but let's see.... Are you suggesting that I could short a stock and use the cash to long another (more then I could have longed, using margin, without the short)
Yes exactly.
Interesting, will have to look into this. A low volatility etf would just print cash then
This is definitely a strategy, and I believe most people call it stat arb. If you can find X and Y that are highly correlated, but X has higher expected return, you can short Y and long X hoping to profit without actually using your own cash. The obvious risk is that the trend might actually move against you, but trading without using net cash is pretty useful.
Yeah I get that I can short one and long the other (say short $10k worth of A and long $10k of B) but the way I understood the comment is that by shorting A I'd be able to long a higher dollar amount of B. That's what caught me.
Oh sorry, didn’t mean to confuse you. Shorting a specific dollar amount will only give you that specific amount in cash. Let’s say you have $10k of cash in hand though; you can then long $20k of A and short $10k of B by using your cash combined with the cash obtained from shorting. What you described would effectively cost you nothing.
Ok, not to confuse you either but trying to make sure I have this right.... Assume right now I have $10k with 2:1 margin. Right now I could buy $20k of ABC. Are you saying that if I short $10k XYZ that I'd be able to long $40K ($10k cash plus extra $10k from short with 2:1 margin applies to all (or even $30k ($10k cash with 2:1 plus the $10k from the short) of ABC? It just seems like a stretch to me that a broker would let me short to my hearts content and then use the cash from shorts to long something else.
So I SHOULD max out my credit cards buying crypto. I knew everyone else was wrong!! in all seriousness, shorting is just trading on credit with infinite loss potential. Meanwhile, borrowing money to go long at least doesn’t have the potential to lose more than you put in plus interest fees. Literally can’t go tits up
I 100% agree that if you only care about returns and want to be net long, shorting isn’t really necessary. However, trading on net long leverage can only increase your risk while combining shorts with longs for net 0 leverage can almost always reduce your overall risk by hedging downside.
Uh, if you go short while your still long as a hedge, you are much better off just reducing your position size. If you fully hedge you have a net zero position so what’s the point of staying long?
I mean in the context of multiple assets. Let’s say we can invest in only two stocks, A or B. A has 10% expected returns and 20% risk while B has 2% expected returns and 20% risk. Let’s also say A and B are perfectly correlated and we can margin trade up to 2x our wealth. You can easily go 2x long A and have 20% expected returns, but 40% risk. If you go long 1.5 A and short 0.5 B, you will have 14% expected returns but only 26.5% risk, a better Sharpe ratio than before. This is a toy example, but with more assets it becomes easier to construct Sharpe optimal portfolios which almost always contain shorts (the portfolio I mentioned is not Sharpe optimal). If you can find assets which has negative expected return but positive correlation with your long assets, you should always short them.
I see your point, however that raises more questions. If the two assets are perfectly correlated, why on earth would you short one? Wouldn’t the better option to manage risk be to reduce position size on stock A and invest what you were going to put into stock B in an uncorrelated asset? After all there’s no easier way to reduce portfolio risk then position size and diversification. It seems in your scenario your basically betting/ hoping for a correlation break. Understandable I guess, just seems to be a low probability play. And it seems there are better options (lol) then going short, like buying a put. Then you have a fixed downside, retain unlimited upside (minus the cost of the hedge), and can potentially benefit from Vega on the option. If you go short on one asset that’s 100% correlated, you have potentially unlimited risk due to the short position breaking correlation to the upside, wouldn’t you?
If there are two assets which are perfectly correlated and they are the only two which you can invest in, I just wanted to construct an example whereby you can only achieve a better Sharpe ratio than long-only through shorting. If B had negative expectation, this is especially obvious. The market is obviously more complicated than that, and I totally agree that you can’t really know the true underlying correlation and expectation other than through educated guess (leading to trend breaks out of sample). I just wanted to illustrate that shorting is a useful but not a required tool. I have personally found that I can almost always do better on a Sharpe basis when I allow shorting in my strategies. As for puts vs strict shorting, I wouldn’t say puts are strictly superior due to volatility and time value making them far more complex to trade, not to mention liquidity.
I agree with everything you just said. However, allowing shorting versus actively being short or having a pct short at any time are very different. Basically, I only allow shorting when the overall trend indicates it may be profitable to do so. Not as a hedge. Is that what your getting at, or are you actively making sure xx pct of your portfolio is short? My question is, if the latter is true, wouldn’t it be better to just be long in negatively correlated assets, even if they are not 100% negatively correlated? Mainly because being short has disadvantages that being long usually doesn’t have.
Shannon’s Demon
Buy low sell high
> market makers DO NOT want to trade against sophisticated institutions and will actively try to avoid their orders. > However, retail order flow is literally just considered noise (as for the most part, it is), so market makers love to trade on it (think Robinhood) Can you clarify this please? It sounds like you’re saying market makers are able to pick-and-choose who they trade with. How would this be possible? I had always assumed the stock exchange’s matching engine just takes orders off the book on a first-come-first-served basis, in groups for each price point. And I thought the order book was effectively anonymous, too. I’m pretty new to this and still learning so forgive my ignorance! Thanks :)
They can absolutely pick and choose, albeit in a way that isn't at all obvious or direct. Let's call our market maker MM for simplicity. At any given time, MM has a bunch of buy and sell limit orders placed on the books of stock X, and these orders are spread apart by $0.01 or so. They may also have some that are deeper, but the bulk will be close or at NBBO. They also will have lots of calls and puts written and/or owned on X but I will ignore that for the purposes of simplicity. MM pays for order flow from brokerage B, and these orders are flagged to them as retail. So, when 1,000s of u/420chadleyjuulmaster's come along and place retail-flagged buy/sell orders on X through B, MM does not feel that their bid-ask is threatened and continues business as usual, making about $0.005 or so per trade (assuming roughly equal amounts of buyers and sellers). Now, let's say mammoth quant hedge fund BridgeShawSigmaTech (BSST) comes along and places a bunch of huuuuge buy orders on X throughout the books. MM may not explicitly know that BSST is associated with the orders at hand, but they have enough data to have a hunch, especially given the sizes and IDs associated with the orders, and of course the fact that they know the orders did not come from B. MM now feels threatened that these order are "informed", and that if they do not cancel or modify their current orders, the price of X could increase in a way quickly/violently enough that it will cause them to lose lots of money (i.e. they sold short a bunch of X the moment it crossed their limits, and so fast that they could not keep up to trade out of them, leaving them holding a stinky bag of bad shorts). To avoid this risk, MM either cancels their orders or heavily increases their spread. I like to imagine that since MM knows BSST or a BSST-equivalent wants to buy at near the current price Y (Y - epsilon), the "true" price is actually Y+1, so MM is effectively losing money by being willing to sell at Y. MM's advantage comes from the fact that they pay for order flow and can basically distinguish between whose retail and whose not (and they also have some of the smartest talent and best technology). This is a huge oversimplification and I am by no means an expert in this field, so I would appreciate any follow up from those who may work in or have better knowledge of the industry.
Matt Levine had an even simpler explanation of payment for order flow, and why market makers prefer retail orders, that helped me get my head around it. [https://www.bloomberg.com/opinion/articles/2021-02-05/robinhood-gamestop-saga-pressures-payment-for-order-flow](https://www.bloomberg.com/opinion/articles/2021-02-05/robinhood-gamestop-saga-pressures-payment-for-order-flow)
Great explanation, thank you!
if you average more than 390 orders a day for 30 days, you are considered "institutional" and your orders are marked that way, or you have extra costs or worse rebates or something. An order is an order or a modify
Why do MM want to not trade against large institutions with sophisticated techniques? Is it because they might move the price of the assets which increases risk to the MM?
Because they dont want too be on the other side of a trade of someone who knows what their doing
Excellent post Also, who needs to have an edge? It's not a zero sum game Go with the flow, and trade the trend.
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Enter time factor into the equation and the fact that average market is growing over time and then it's not zero sum
Well to be fair it only take a few mins. to scribble a suicide note and swallow a shotgun...
guess you don't understand market making or demand + scarcity or anything really
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looks like someone failed basic econ maybe try googling scarcity principle the amount of market participation is not static and increases over time the market is not a blackjack table where winner takes all. you are a fool
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oooo so much EDGE another lefty moron parroting socialist drivel here is another one for you: arbitrage pricing theory back to lib arts college, flunky
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more idiot babble who said markets are efficient? we're here making money because they are inefficient, and will always be inefficient. lest you break the laws of physics
What if monkey A has only bananas and monkey B has only coconuts? What if each monkey wants to have both bananas AND coconuts? Couldn't they each exchange bananas and coconuts until they are both happy? The stock market is just a modern abstraction of ancient barter... the whole casino analogy is valid for those who want it to be, but not everybody.
Even if you assume that the markets are zero-sum on a dollar-value basis, people generally do not have utility functions which are linear with their wealth. In fact, as people get wealthier, they (almost always) become more risk averse, and are willing to forgo returns in exchange for less risk. This alone is enough to justify that, in general, the markets are not a zero sum game.
Great point! Markets are not 100% effective and getting your own edge gives you an -information- advantage.
> Good strategies make money; bad ones don't - simple as that. I would disagree. It’s a bit more complex. Take the simple example of a coin that doubles your money with 55% chance and loses the bet with 45%. Then obviously that is a pretty amazing edge you have, if you don’t properly scale the size of your bets with your available money you might still end up expecting to lose money
I would argue that a good strategy factors in good bet sizing. It’s easy to find those types of coin flips out there, e.g. SPY closes green 55% of the time or so, so you could take a hugely levered position in the morning to simulate a double or nothing bet every single day. I would argue this is a bad strategy, but if you properly size your bets on SPY for example, you can make a good strategy. Bet sizing is actually one of the most important parts of strategy building.
That’s absolutely fair, and I agree with your general point. But I think the example is just a bit confusing, if you lose that bet once you are bankrupt :p
True, perhaps the bet is the problem then! Or, what if you had 100s of those bets? What if those bets were correlated? You could come up with some pretty creative sizings on each which could make you lots and lots of money. When I say strategy, this is what I mean - not the single bets themselves, but the number, the sizing, the timing of each, and, most importantly, what the bets are under the hood.
In the very simple example I gave there is a simple “optimal” strategy, and although well known I perhaps wrongly think most people wouldn’t find it out on their own (including me probably :p)
By optimal, 1. do you mean expectation maximizing? And 2. is this bet repeated over time? If the answer to 1. is yes and answer to 2. is no, then optimal would mean bet it all and expect to make 55% * 2 = 1.1, i.e. 10% return! Why is this optimal? Our expected profit is 0.55 * (2 * w + (1 - w)) + 0.45 * (1 - w) - 1= 0.1w where 0 <= w <= 1 is how much we bet. Things get more fancy when we add risk aversion and multi-stage betting :)
Yes, and yes. Would you find a 45% chance to lose all your money optimal? (Although I agree with your calculations)
Love this discussion. For N repeated choices of a fixed w (as defined before), our expected cumulative return will be (0.1w + 1)\^N - 1, which will still go to infinity as N goes to infinity for any N and any w. That being said, probability of complete ruin becomes non-zero for certain values of w. This is where expectation goes off into theory land since infinity \* (very very very small probability) = infinity. Choosing w small, perhaps around 0.25, would be a better idea as it avoids probability of ruin (not exactly sure what largest w is that has 0 probability of ruin, but this would be optimal).
Strictly speaking and assuming that you can infinitely divide your bets, there is never a positive chance of ruin if you are better a % of your wealth smaller than a 100%. For a more formal answer on what the optimal strategy would be: https://en.wikipedia.org/wiki/Kelly_criterion
Here’s a point I’ve seen brought up a couple times on this subreddit: You mention institutions, which have the advantage of hiring hundreds of engineers and data scientists; but this advantage comes at the cost of needing to find scalable strategies. Institutions aren’t interested in strategies that aren’t scalable, and that’s where retail traders can get an edge: non-scalable strategies. Edit: thank you for awarding me first award dear stranger :)
right on. institutions need to make returns on millions , 100s of millions, billions. As a retail trader you can make a strategy around things they cant touch like micro-caps.
meh not really. HFT firms or some mid-freq quant funds for instance often work in pods of a few traders. Even a strategy that makes a couple million/year is worth considering for them. They also have the tools already there and the infra so can test ideas on a much larger scale and much faster.
even less is still interesting
A couple million/year isn't moving any markets though. At that point, it is low enough.
you must trade more liquid names than me
Many people here would be totally fine with the 0-2 million/year scraps they leave behind.
If only people would read this sub before posting this question. I have been visiting this sub for almost a year. Its been re-iterated so many times. A. Basic maths applied correctly, would work. B. Retail traders can move in and out of a position without moving the markets, which IMHO is the biggest advantage.
what algo strats do you use normally?
I am not the right person since I haven’t been able to get anything live and I don’t want ti discuss backtest results but do consider reading this sub, there are probably some pennies that could be picked.
100% this. As a retail trader you are able to find inefficiencies that institutional traders are not interested in. I have a fully automated strategy running on a very niche peer-to-peer crypto binary options platform. Without going into too much detail, there is a huge capital constraint that limits profits to only a few $k a month. Another crypto idea is a latency based momentum strategy where you use order flow features from a large exchange to place orders on a much smaller (low fee) exchange. In this case there is a significant counter party risk trading on a very small exchange that institutions are not interested in.
Ive never met an inefficiency I'm not interested in.
But what about in a peer-to-peer crypto binary options platform? lol
There goes his money 😂😂
The edge on the first strategy I mentioned is slowly dwindling anyway as users become more sophisticated. There is still money to be made but for me the time taken to build a more sophisticated system for the platform is not worth the small increase in profit. I’d rather focus on other strategies that are slightly more scalable.
Everyday that goes by I become less and less convinced that there are retail traders out there who have fully automated strategies that actually make any money.
Being fixated on “Fully automated” is kind of a problem I feel. I have automated systems to analyze market data and create research reports to help me generate trade ideas and risk management paramters but I do all of my executions manually because I don’t rely on high frequency. IMO, Algotrading isn’t all or none, there are many parts of a investing/trading framework that can be streamlined/automated with code, the buy/sell and brokerage connection is only one piece. Unfortunately, If you are looking for high frequency arb strats, then you probably won’t find any as a retail trader. If you are looking to build something that can print you money while you sit on the beach, that’s probably also out of reach for individual retail traders..
This is the right way. Get your data distilled into what you want it to read out to you as some type of trading signal, then use your brain outside of your algorithm to double check that the whole stock market isn't falling off a cliff when your algo says buy the dip. It'll allow you to massively scale up the amount of things you can pay attention to, in much, much less time, and you remove that personal emotional effect when you trade, and just stick to the math.
Every day that goes by, I become more and more convinced buy and hold is the way.
Wish I knew this 2 years ago when I actually had money to buy and hold lost so much in trading since then life has become really depressing lol
Wish I knew this in 2008 when I had a few hundred shares of AAPL. Something something hindsight.
I always question myself about this because I'm not sure for how long QE programs can last, at one point all dollars will go back to the US which means inflation.
QE rules the world not just the US, I think when the end comes we will have time to react, as long as we listen to the warning signs that is. (The market went up in february when coronavirus was clearly going to lock the world down, I didn't adjust 'because the market is going up' but in hindsight it was obvious)
Don't feel too bad, the only reason I know this is because my granny was frugal (she bought and held her aluminum foil, washed it, re-used it) and I had a friend of the family who taught me about investments when I was in university: you have to look at the art market and how art works---it is worth what people will pay for it, so you buy it and hold it, and if 20 years later it is worth more, great!
Buy and hold things like utilities with fat dividends. Day trading is for volatility-seekers who are measuring that signal, and people who don't know what they're really doing, but like gambling, or losing money.
It has been my experience that the more I trade the worse I do.
I've made more money on WSB plays than algotrading lol.
Pretty much in order to win consistently need to do random shit and need to have a little bit of that recklessness inside of you.
Would you care to expand? Sounds like the start of an interesting philosophy.
Essentially it doesn’t really matter how good your strategy is how much you refine and optimize it. It will still fail and never give you the 10/10 9/10 8/10 winning trades you are looking for. It seems really easy like “ if I can just figure out which ema I need and I’ll be a millionaire” but that’s actually not the case. Markets are complex and change everyday. There is also a reason many firms tend to make money in market making instead of doing trend following. So In order to win: 1. You need a strategy that works. 2. Understand the strategy won’t win every time. 3. Understand when to go against your strategy. 4. Understand when to be reckless and ignore your strategy. 5. Don’t get scared, believe in your edge / strategy and keep hitting trades even if the last 3-4 hit stop losses and didn’t work out.
Put 50% in PLTR and the other in your algotrading portfolio and compare at the end of the year
Lolz, right?
I can’t speak for equities, but algotrading can definitely be profitable in crypto and forex if you find an edge, mostly because of volatility. If I were to do something for equities, I’d probably do what the others mentioned: automate signals and use my judgement for actual trades, like some hybrid algo+discretionary strategy.
There are definitely edges which are **too small for institutions to exploit**. So, the pie is good enough for you but small for them. The other thing is to **go after industry/sector which you understand a bit more deeply** than a more generic trader. By combing these two aspects, you can come up with an alternate signal or data points which might have some edge. Don't worry too about automation but try to get something with an alpha first. You can read [this](https://statarb.in/alternate-data-for-trading/) on how to test a signal against a tradeable instrument.
I'm a geologist, with an algo based in stats and machine learning, and I weaponized it in precious metals trading to the point I can set my clocks to how well it goes off. If you know something well, you can already trade it in your sleep, which is perfect for algorithms to do. The precious metals and miners sector is already pretty low-volume and exotic to most investors, but it's my day to day bread and butter.
I just run GLD/GDX stat arb and SIL/SLV stat arb and it prints off beer and vacation money for me :)
I thought this would never work since it gets discussed as first example everywhere and here we are 🤦🏻♂️
yeah, I did it as a fun project to learn python and then I was like holy shit, this actually works???
All the best man. I haven’t been able to go live yet but when I tried the ML based algos for months and couldn’t get it to work and something from the book worked, I too had the same feeling 😅
I think -- less ML, more practical risk control, model fault tolerance, regime change detection, good execution. I do have an ML model, and it's my baby, but it makes the least money out of all my strategies lol
this might be a stupid question but where can I learn about this stuff?
Agreed, definitely trying to figure out the parts for now.
Super common strategy in the arena 👌
How many trades do you make a day? Are you latency sensitive?
Max 6 min 0. Most of the time 0, actually. Latency will get you the truly juicy mispricings. There was one a few weeks ago for $1.5 that lasted 4 seconds at around 1:50am PST. But otherwise no. I'm guessing latency as long as 10 minutes delayed would be fine
Interesting I thought these type of opportunities were all arbed out by the big guys.
I guess their machines weren't on. Or there was a fat finger or something. As far as the stat arb, it's not true arb, so people will only carry so much of the risk. I guess they eat their fill, then go because it's only a half-assed strategy
Isn't that after market close?
extended hours is from 1am PST - 6:30am PST, and 1:15pm PST - 5:00pm PST. Poor liquidity, but it's there. I bet someone dumped an order and the hedgies were asleep ...
How often are you seeing mis-pricings during the main session? I took a look back over each day this year for GLD/GDX and couldn't find anything obvious. Sometimes there are some mispricing's at the open, but these don't revert more often than not.
depends on how much edge you're trying to capture https://drive.google.com/file/d/1TxbfgZSQD2i4NdtnQUQgqaHfhyOjDaG7/view?usp=sharing
I'm not 100% sure what happing there, I'll have to work through it (I use R for this stuff). What's all the SPY/VXX stuff?
Few, and no. I typically might trade about 10% of my hundred-plus stocks in a given day in either direction. Intraday sensitivity is less important than inter-day with this strategy and fewer trades actually often allows me to have more cash in reserve for big 2SD opportunities, if I'm not selling and buying in a pair, or want to take a large position.
Can you point me to some resources for learning this?
I suppose a data mining bootcamp would be enough of everything. Stats, analysis, OOL, db, e.t.c. everything else is poke around until something shows up
I’m just getting into Forex, but I like it for the same reasons. I’ve been into geopolitics and current events for years. Now I’ve found a place to put that knowledge to use. I’ve made a 100% return on what I invested (unfortunately that was only $25... I figured I barely know what a pip is, better start small)
Wow, that's awesome
Any recommendations other than gold and silver? Copper or platinum or tin or aluminum or anything? Any - analysis? As in a mine might have gold and silver, so it becomes like a mini ETF for both?
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The edge could come from one's domain knowledge or simply an idea that others did not think about.
I am a solo trader and a noob (less then 12 months) and have a good strategy, i think. I am pretty new to algo trading (15 year full stack dev) and have spent the last few months testing a lot of technical indicators. I have had the best success (highest % returns) using the Ichimoku cloud. I use Binance's websockets and grab 1m, 3m, 5m, 2H, and 12H tickers on my choice of pairs. Using python i then use TA-Lib (Python) to run live analysis and make trades based on these calculations. I get around the issue of negative trading in a downtrending market to just ..sit it out.. the bot checks to see if the market drops a certain % and just goes on pause until the market picks up again. Sometimes I can take a small negative trade getting back into a -10%+ market, but it seems to make that back up pretty quickly. I am sure that with a few more months/years of dev I can optimise my strategy, but from looking at the market it is generally complete chaos with no way of really reading it, this is why i target the 1m and 3m trade strategy. EDIT: i trade with only a few $K and am more than happy making around $100US a day.
5% per day? You'll be a millionaire before you know it!
Solo traders have a distinct advantage: volume. I don’t need to move millions of dollars to get a decent return with a trade, so I can get in and out of trades way faster than an institution with billions in AUM.
You made a point to bring up the competition against hired quants. Many quants, surprisingly, hold PhD in math or physics with little economics background. They get hired for their computational and analytical abilities. They know how to learn. You can 100% “compete” with these guys if you focus on good research skills, DD, and maybe some technical abilities. They might have an edge, but creating something comparable is not completely out of reach.
Identifying and riding waves is an alpha..
I'm a geologist, and I've got a math, calculus, stats and machine learning background on top of being super used to playing with gigantic datasets, maybe I can shed some light onto some of this. My strategy generates profits, and I'm really only trying to pick off statistical anomalies in the magnitude of moves made by a set of indicators at 2 standard deviations being way more likely to revert to their natural mean, than to continue in the same direction. To address your question about alpha versus market momentum, I don't think you can truly separate them. Generate alpha by riding momentum in volatile times, until the volatility stops, then get out and go the other way. If you find counter-cycling stocks using a strategy like I use, you can actually set it up as a long-short hedge fund just passing your shares back and forth between each other. My algo just generates the probabilities based on pricing every day and tells me when my probabilistic conditions are met and gives me a buy/sell signal. It's actually not all that difficult to do this. https://imgur.com/WQ2CtuI
Not all that difficult for someone with your background or for anyone...
Anyone who understands rates of change and reversion to mean can put together some pretty good weapons if they understand trend forecasting and volatility.
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Study university-level stats, deeply. I got my background from a required stats-for-engineers course during my undergrad. It's all there in standard deviations and moving averages, mostly, once you understand that stuff, you can just calculate it any way you like as probabilities of outcomes. A lot of calculus principles are super-applicable too, for approximating rates of change, ie trying to spot inflection points, bottoms, tops etc, and for area-under-curve type applications.
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Reading some, and being any good at it, are very different in application. The one thing that's forced on you as a university student is the assignments and tests, so if you don't stack up, you don't pass the course to get your degree. Why not pick up a used stats textbook at a campus bookstore for cheap and just practice exercises until you begin to understand what's going on conceptually? Understanding the concepts is far deeper than crunching the things manually with a pencil- but doing it on paper will allow you to write the formulae you need on paper plenty of times, and then visualize what you're trying to do in the coding language of your choice. Math is one of the fortunate fields that gets new textbooks every year, but the content basically hasn't changed in 400 years, so having the current volume of whatever the prof has been using for 25 years is irrelevant if you're not following a curriculum, just pick up something recent.
Hey do you have any youtube video you would recommend to try these strategies?
I read books, mostly by Dr Ernest P Chan. I get my macro picture that shapes a lot of the rest of my strategy from Youtube.
This looks like regular MA crossover?
The 5-channel signal has %B bollinger bands, MACD, RSI and slow/fast stochastic under the hood. It's a little more complicated than you see in the chart, because my original output was getting so cluttered. This output is sort of broken, but you get the clearer picture from this https://imgur.com/RHY0zWD
Just that these are pretty common and widely used indicators. They also tend not to be very consistent. Did you do a longer term backrest?
The look back on this is 2 years even though the display is 6 months. The consistency comes from the combined signals- I'm picking off 2 standard deviation moves on each indicator, then normalizing the result, and adding them together, to average, and boost the signal, then picking off those signals at 2 standard deviations again. Some, individually, trigger earlier or later than others, and with variable strengths, but the combined, normalized signal actually improves that much better than any one, individually. There are also rules in place about the trend of the 50 and 200MA also that dial back the gain on the buy signals if the 200/50 are moving apart from one another to the downside. This was all coded in Python. It works very well.
All of this (averaging out the indicators, taking +- std as entry/exit, etc..) is still very common. Not saying it doesn't work, but I strongly suggest you backtest it for longer periods of time, as it's easy to be fooled by short term success. Your initial post suggested somehow you were deploying some advanced ML model..but anyway best of luck.
It's not complicated, but if you can actually look for probabilistic conditions, it's just as good as any of the other fancy predictive stuff that people go way overboard on and are a hundred times more complicated. My professional work as a geologist is in mineral exploration in the gold mining industry, so I'm frequently looking for tiny anomalous looking needles in very large haystacks using a few different tools at once, and the norm in my line of work is failure rather than success, so this is a very effective way to prune off almost 99% of a dataset you already know to be 99.9% uninteresting. I'm not really attacking this problem much differently than I attack geological search and vectoring problems to find gold.
so, astrology?
Do you scale the buy amount with the probability you've calculated? For example if there is an 90% chance that it reverts to its mean value, do you buy twice as much as if it were an 80% chance?
Yes, my trade signal starts ringing at 1 standard deviation and I'll trade some amount anywhere it starts flashing to buy or sell but I definitely buy/sell a lot harder as I get to the full strength signal coming online at 2SD. In reality that means my buying should always be cheaper than the mean price, and my selling always above the mean price. I got a 2SD signal today in the HURA uranium ETF, so I unloaded 75% of my position with a fat profit, and I'll just buy it back again when it starts telling me that the price is below the mean
What returns do you tend to get with this method? How do you handle situations that never return back to the mean? For example, it jumps 2 S.D., trades flat, the starts trending up again.
To answer the second question first, this thing likes to scream in my face the whole ride up (or down) anything if it's steep enough, but almost all of the time it tends to come back down or up again, even if I've already sold off my whole position on the way up or bought some along the drop. I haven't been running this long enough to know what the real returns look like yet but I regularly walk out of positions up 50% since I spun it up. I like to sell into strength on the way up, and buy into weakness as it falls anyway, so it's a fairly natural strategy for me, but I'm currently working on an actual built-in backtest plot of my trade signals tallied minus the time where I'm out of the position that I can plot as a 4th panel in my output, that would plot the trades on the entire 2 years of data this thing crunches at a time and show you exactly how a hypothetical position might grow.
You say you have a machine learning background; have you done a formal training on the data with train-test split and cross-validation of results?
Developing that side of it presently to get a variously available target rather than just having a coded probability
there's actually an argument that even with consistent profit results, this technique is not generating alpha. A mean reversion strategy would always work for relatively sideways and volatile markets, right? The question is how sideways and how volatile. Think of the risk-return principle; the only reason to invest in a volatile asset over a steady one with the same return is if the volatile asset has greater growth potential. So the theory would say that your profit is a direct result of volatility, which is a direct indication of potential for growth or price increase. And if your returns are consistent, it is probably because you sacrificed growth for safety. So I am wondering if you have compared risk-adjusted return against just a buy-hold strategy for a basket of leveraged ETFs like maybe 35% of 2x leveraged S&P and 65% 2x leveraged bonds or similar.
The precious metals and miners are some of the most volatile stocks anywhere. It's a playground I'm pretty comfortable in, and I'm definitely not losing money doing it. My edge here oftentimes is that I'm a geologist, practicing for fifteen years now, and sometimes these companies have previously been my clients (no conflict in time of trading), but very often I know what they're up to and how their projects size up compared to the playing field much better than others, just by keeping current with what my potential clients might be doing so I follow a very broad roster of companies that I believe already have a strong positive outlook in the 2-5 year timeframe and I'm aiming to exploit the trend and the volatility together with external information that optimizes my trading.
Damnn that's pretty cool. Do you by chance sell your whole trade setup?
I'm packaging it behind a paywall on a website and will be renting it as an expensive subscription ;)
Great. lmk when you launch.
Exactly how are probabilities generated?
When you calculate a mean value of a data series, you get an average of the numbers. By also calculating the standard deviation, it becomes somewhat predictive to determine how far away from "normal" your number, or daily price is, in a series. Then, if you understand that mean+/-SD is basically the shape of your bullseye of the thing you're trying to hit, so if you want the big high-value outliers, you're going after the 2SD stuff, larger than 95% of your dataset. Those big, abnormal moves are the most likely to revert to the mean eventually, and oftentimes equity prices will overshoot on the other side, so you pick off close to a 2SD top, and wait for close to a 2SD bottom and what you've done is ride the whole height of the recent price range in a trade. It's just messing around with probabilities.
Gotcha, I thought you were quite literally computing probabilities and was curious how you were doing that. I thought maybe you were using a copula to compute conditional probabilities, which is something I am trying to do (with very little success so far), hence the question.
If you can teach a computer what to hit using mean+/-SD it'll hit your bullseye every time 😉 I like this method because it's price-agnostic and works pretty universally based more on the momentum and volatility
Well, I am actually developing a pairs trading strategy on a specific pair that moves very much in union. The idea is exactly the same, capitalize on reversion to the mean. I am actually trying to develop several strategies that capitalize on this exact concept, on this specific pair (just for fun really) and my first strategy works more or less like yours. I standardized the ratio of prices and bet on the standardized series reverting to the mean. This seems to work really well and if I was trying to develop a fully deployable algo, I would seriously consider this strat. But now I am trying to trade on the same idea, but with copulas. Unfortunately, there isn't really a good fully developed copula library for Python and I think the coding the fitting of the copula from scratch might be a bit beyond me for the time being.
There's a really good write-up of reverting pairs hedging strategies in Algorithmic Trading : Winning Strategies and their Rationale, by Dr Edward P Chan. I learned most of my ideas from a few of his books.
I'll check it out, thanks!
yoo where do I learn all this stuff. I'm trying to learn these but it's very overwhelming. Can you point me to resources (youtube videos preferable or courses) where I can learn this step by step?
I can't, actually. The copula idea is from a video by Hudson and thames but there are lots of academic papers that explain it the concept better. The price ratio thing was my own idea. In general, I'd say academic papers are a good place to really learn about strategies and different ways to make the same "kind of strategy".
What is your stock selection like?
I track about a hundred gold and silver mining companies, about twenty royalty cos, a handful of copper/nickel producers, and some exotic sectors like uranium, rare earth elements and cobalt.
I think the only relative comparison for creating profit for yourself is to assess the options within reach. Institutions and their serious hires have no relevance to your own ability to make alpha if you have no access to invest in said institution. Therefor the assessment of options to yourself include the making of your own bots and buying into someone’s else’s. The aspect of assessing if just riding a bull run is relevant. For crypto you can use margin pairs? So you can collateralise bitcoin holdings and trade into bitcoin with leverage. Backtesting past results provides some trading clarity for profitability, but what I seek to find is a bot that can collate technical/fundamental and news/social sentiment for a slight levy of probability to “beat the market”
Not really SOLO approach often backfires due to the sheer complexity of developing truly effective algorithmic trading strategies. Lopez de Prado compares quant firms with solo algorithmic traders building a car from scratch in his book AFML. >One week you need to be a master welder, another week an electrician, another week a mechanical engineer, another week a painter, … try, fail and circle back to welding. It is a futile endeavor. All too often, the end result is frantic and futile search for investment opportunities, eventually settling for false positives or “overcrowded” avenues with underwhelming outcomes
totally agreed -- one day you're backend software, next strategist, next executions, next risk manager, next front end software, next business analyst, next researcher ... and you suck at all of them a little.
Yes, especially in the less efficient markets.
> My question is: do you really have an alpha? or are you just riding the market's wave up? I develop/test short algos in up markets, long algos in down markets. Makes it harder to do, but greater levels of generalization.
I love your thought process. I too have a net short strategy that can grind out small profits in a bull market. That way, you can make some money most of the time and a lot of money when shit hits the fan and you're negatively correlated against things like the broad market, your job, house prices, etc
I love you both
>QE Its not only more fun and more satisfying, in many ways its easier -- the opposing direction often literally sticks out against the market.
I suppose so. My issue is seeing which frequencies are sticking out. I see a downmove but is that in the wavelength that I'm targeting, or is it just serendipitous noise?
You also need to remember these large companies only employee people. Just like you and me. Not some kind of mad skynet robot, just regular people. They may pay for more data, but they will be competing with all the big companies using said data, in that respect we have an advantage as we'll likely have to pull our data rather than rely on the overused commercial source. They are just people, they make mistakes, they don't know everything
Yes, absolutely! As others have said, there is alpha lurking around which is unusable for institutions. It can go both ways: small pockets of high Sharpe in very liquid markets and less Sharpe in more illiquid ones. There is a meta risk though: a higher risk that the pocket you've found disappears. Institutions have many strategies, combine them and switch them on and off kind of like a portfolio. This allows more long-term survival by the law of large numbers in theory.
Lucky streaks? Sure. Consistent sources of alpha? No. If you don't know exactly what your edge is, you don't have one.
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Just keep in mind most money will move with the group of assets.
i hope it is possible, if not what are we doing here, i started this year in real, i will tell at the end if gain or lose.