
I've reproduced 130 research papers about “predicting the stock market” - starpilot
https://www.reddit.com/r/algotrading/comments/cr7jey/ive_reproduced_130_research_papers_about/
======
starpilot
Spoilers:

> Literally every single paper was either p-hacked, overfit, or a subsample of
> favourable data was selected (I guess ultimately they're all the same thing
> but still) OR a few may have had a smidge of Alpha but as soon as you add
> transaction costs it all disappears.

> I should caveat that I was a profitable trader at multiple Tier-1 US banks
> so I can say with confidence that I made a decent attempt of building
> whatever the author was trying to get at.

~~~
DennisP
But also:

> Almost every instrument is mean-reverting on short timelines and trending on
> longer timelines.

i.e. he confirms the momentum factor, which isn't surprising since there's
more solid evidence for it than anything else, going back hundreds of years.

He doesn't say what fundamental factors he looked at, so it's possible that
value, size, and profitability/quality would hold up as well. All those have
been studied pretty extensively in academia, in papers going back decades. The
author took only a fairly random sampling of recent papers.

~~~
edoceo
All the finance folks I know use "fundamentals" to mean some class of
attributes, that they all know what they are. Like when I say "GPL3" to
someone who has a-priori knowledge.

Not exactly sure but I think the "fundamentals" are top-line revenue, unit
cost, unit margin, yoy-growth, EBTDA, cash-on-hand, default-alive - and likely
the ratios those values produce.

~~~
lootsauce
Fundamentals mean zilch for trading unless your'e talking earnings
announcements or like news.

~~~
H8crilA
Please don't downvote this comment. There's a huge difference between short
term trading and investing. And yes for short term trading (minutes/hours/few
days) fundamentals matter rather litte - after all you can be a successful
trader of Bitcoin, which has pretty much no fundamentals. Except on volatility
events like macro data releases, earnings, FED decisions, surprise news
(looking at you GE) etc.

~~~
Retric
> fundamentals matter rather litte

Having a small impact is not the same a having zero impact.

Traders operate over longer timeframe even if they are holding an individual
stock for minutes there is a limited pool of stocks. Keep playing the game and
longer term impacts add up.

~~~
derefr
But do the fundamentals of an individual investment matter in the short term
compared to the fundamentals of the market as a whole? I would suspect that
the “longer term impacts add[ing] up” would just be market health. Day-trading
randomly-picked stocks with random buys and sells is a Markov approximation of
an index fund :)

~~~
Retric
I think that depends on how stocks are chosen when you are day trading.
Limiting things to high volatility stocks for example creates a bias in your
index fund approximation.

------
lettergram
I had the same result from most papers. My personal conclusion is that when
people get a winning strategy they don’t publish. I personally put my money
where my mouth was for a few years:

[https://austingwalters.com/backtesting-our-100-yoy-profit-
ge...](https://austingwalters.com/backtesting-our-100-yoy-profit-generating-
strategy/)

That being said, I try to be honest too. This can disappear any time and the
model I use may only be good in this environment. I do not know. I think
that’s the challenge with papers, you don’t honestly know when or if the
strategy works. It clearly won’t forever regardless.

That’s why I don’t share my exact method. And after doing all the research
myself AND trying to sell my algorithm. I honestly don’t think the industry
knows what it’s doing either. People are worried about sharpe ratios and all
this BS stuff. The reality is for these models you mitigate risk via temporary
and ever changing methods. Can’t really publish on that.

~~~
p1esk
If your algorithm was successful, why did you try to sell it?

~~~
semi-extrinsic
Just a guess, but: if you come up with a successfull algorithm, you still need
to have money that can be invested in order to use the algorithm. So maybe
someone else with 100x more money to invest would pay more for the algorithm
than you could earn from it in a lifetime.

------
1e-9
In my view, the predominant mistake made by those who seek to create
profitable strategies is that they approach trading as if the market is a
zero-sum game. In particular, doing things that harm the markets, like naively
adding to existing momentum, is just promoting price overshoot and instability
by reinforcing positive feedback loops. Such approaches hurt others and while
they might make money for long periods of time, they will almost surely end up
losing all that profit and more during a small number of extreme market
events.

If you want to be reliably profitable, you need to first understand how the
markets are not a zero-sum game and then you need to construct methods to
improve the markets with your trading. There are countless ways that markets
deviate from truly efficient behavior. Find some and develop strategies in
areas that can benefit from your cognitive, experiential, and educational
strengths. General examples of how one might improve the markets include
things like providing liquidity when it's needed, limiting price overshoots
when it's warranted, and incorporating new information about instrument
values. The market will pay you in return if you do such things in a sound
way. As long as you also do a good job of estimating and limiting your risk,
you can be consistently profitable.

There will be no significant public disclosures of detailed ways to trade
profitably. The markets rely on the robustness provided by many different
points of view addressing market needs in a variety of ways. Any parties that
overweight one of those points of view will ultimately lose money in the
process of adding market instability. There's too much of that already. Don't
trade until you figure out how to make the markets better.

~~~
anonu
> you need to construct methods to improve the markets with your trading

What percentage of traders do you think will approach the market with such
altruism?

I think the markets are way too complex to rationalize "improving a market".
Markets are by definition good markets when you have long term and short term
traders mixed in with technical and fundamental traders all with different
alpha time horizons. This is a healthy market.

If you're a speculator, so be it. As you point out, the market may teach you a
lesson at some point. Those guys go away but new ones will join.

It's a beautiful virtuous cycle.

Not to say there aren't bad actors. Most speculators are not IMHO. But they
cross the line when they undertake certain market activities, like spoofing
for example. This is why good markets also have good regulatory oversight
(finra, sec... )

~~~
1e-9
> What percentage of traders do you think will approach the market with such
> altruism?

Approximately 0%. I don't know of any entities that trade as a nonprofit. The
point is that if you find something the market needs, you will have inherently
discovered an opportunity that can make money because there is demand which
exceeds supply for the service. As an additional benefit, such strategies are
unlikely to run afoul of regulatory oversight or be eliminated through future
rule changes.

> I think the markets are way too complex to rationalize "improving a market".

This is not rationalizing, it is about how to effectively identify profitable
opportunities. Let's turn the argument around. Do you expect a trading
strategy that harms a market to be reliably profitable without risking fines,
banning, or imprisonment? If you don't, then it probably makes sense to
exclude such approaches from your strategy search space. Additionally, I would
argue that strategies which are of neutral benefit to the markets are likely
to generate small returns relative to their risk because entities on the other
side of your trades are not receiving value and thus the market is more likely
to turn against you. If we eliminate "harmful" and "neutral", we're left with
"beneficial".

I completely agree with everything else you say.

~~~
astrophysician
> Do you expect a trading strategy that harms a market to be reliably
> profitable without risking fines, banning, or imprisonment?

Non-trader here, what do you mean by a particular trading strategy "harm[ing]
a market," how would you actually measure harm to a market by a given trading
strategy, and would whatever metric you decide on for that not just be a
dressed-up subjective argument?

~~~
1e-9
By "harming a market", I mean "causing a market to be less efficient". It is
typically easier to identify harmful behavior than it is to quantify the
amount of harm caused. Sometimes you can easily estimate a rough lower bound
on the harm's cost. Easier cases include 1) Spoofing, where you could estimate
the harm as being greater than the captured profit, and 2) Self-trading for
the purpose of receiving liquidity provider incentives, where you could
estimate the harm as being greater than the incentives received. Harder cases
include situations where poor strategies caused market instability by
underestimating risk or trading in a way that is too similar to others.
Extreme examples of these include the Long-Term Capital Management (LTCM)
blowout in 1998, the 2010 Flash Crash, or any number of firms that contributed
to the financial crisis of 2007-2008. You should be able to find plenty of
academic papers attempting to quantify the harm of those.

------
edwardy20
Professional quant here. I have to say I strongly disagree with the
conclusions of the OP.

> They were all found by using phrases like "predict stock market" or "predict
> forex" or "predict bitcoin" and terms related to those.

Yeah, searching for any finance papers with "predict" or "machine learning" is
literally the lowest quality tier you can get. These papers are often written
by grad students who can pump an easy paper out by "applying" some already
known ML algorithm to financial markets. Of course it's not gonna work. It
also kills me when I see ML models who need stationarity assumptions applied
to non-stationary time series data. Yeah, good luck with that.

THAT being said, there is lots of high quality research which has been
replicated over and over, showing that alpha does exist in the market (and
which funds have made billions off of). I would like to see the OP try to
replicate some of these instead. To give some simple examples:

1\. Try searching for papers with the keywords "and the cross section of
expected returns". For example, the momentum factor which can be tested and
replicated with only linear regression. > There is substantial evidence that
indicates that stocks that perform the best (worst) over a three- to 12-month
period tend to continue to perform well (poorly) over the subsequent three to
12 months.
[https://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107)

2\. Statistical arbitrage strategies which were known to work well until the
mid 2000s. Also been replicated many times, furthermore, you can see the
gradual decline in profitability pointing to the theory that "alpha decay" in
this case is real.
[https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatA...](https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf)

3\. High frequency strategies. No way OP or any retail trader can replicate
this, but firms make billions of dollars per year consistently doing this.

In conclusion, to make a claim that there is no alpha in the market seems
highly suspect, and perhaps just needs a more nuanced view of how trading
firms make their profits.

~~~
gillesjacobs
I also find it highly unlikely anyone is able to implement 130+ papers in 7
months.

This would require insane productivity, implausible access to pricing and news
data resources (which are often not freely available) and expertise in machine
learning, natural language processing, finance, and data science. OP had to
implement financial, time-series and linguistic feature engineering pipelines,
as well infer the architecture and hyper-parameters used AND train all these
models.

He also claims he "web scraped" all the data which is highly unlikely as
pricing datasets are often sold for a pretty penny and not publicly available
in the detail described in several of these papers.

OP must be a genius to pull this off, all the while being a trader at "a Tier
1 US bank" (in itself that description is ridiculous).

All OP has to show for all this work is a hastily written Reddit post with
dubious claims. There is no proof of the work done whatsoever, no code
samples, not even result tables or graphs. And at the end OP chills his
cryptotrading bot.

What's worse HN seems to gobble it up naively. Seemingly because OP is
critical of something that is popular to criticize.

~~~
lordnacho
> OP must be a genius to pull this off, all the while being a trader at "a
> Tier 1 US bank" (in itself that description is ridiculous).

Mostly agree with you, but what's ridiculous about that description? His
LinkedIn says he worked at Merrills and Citi. Those are normally considered
top tier US banks?

~~~
anonu
If those aren't top tier I don't know what is...

Generally the banks are no longer the place to do prop trading though. You're
better off at a hedge fund. They will not only pay better but have way better
access to resources, tech, experienced traders, etc

------
bArray
Currently I remain skeptical, 130 pages in 7 months plus meaningful
experiments is quite some going. A list of the papers (so at least the authors
can defend themselves), the source code and data used (because some of these
methods require social media inputs) would definitely help.

After doing so much work though, why wouldn't you go the extra small few steps
to publish? That way the work can be peer reviewed and the Scientific
community has a chance to learn from it.

~~~
bem94
> That way the work can be peer reviewed and the Scientific community has a
> chance to learn from it.

After trawling through so much peer reviewed work which in their view is
utterly broken, I can understand why they'd be despondent/mistrustful of
submitting it to a peer review process.

~~~
bArray
If you want to go that route, why not write a detailed blog post? Or a white
paper in an archive? Or a GitHub repository? Not putting anything out there
seems awfully unproductive.

------
lootsauce
Best survey of the subject I have found (most are bullshit) is Finding Alpha
by Eric Falkenstein which he has graciously offered for free on his blog. By
the subject I mean, finding an edge in trading. Spoiler alert there is no
system to follow. He wrote algos for a local options market maker, had a
significant Econ PHD thesis. The basic premise of the book is that real Alpha
is rare, idiosyncratic and gets exploited by those in the know and eventually
the edge goes away after several years. He gives several classic historic
examples which are what made this book so interesting and unique to me.
[http://falkenblog.blogspot.com/2016/08/finding-alpha-
pdf.htm...](http://falkenblog.blogspot.com/2016/08/finding-alpha-pdf.html)

------
sixtypoundhound
I'm not surprised by this finding; too many smart people are working on the
same problem.

When it comes to investing in public markets as an outside player, I've seen
three moves which can work....

1) Identify a consumer product category going through the technology adoption
S-curve, ideally something which isn't very subject to short term innovation
cycles or disruption.

My wife spotted the trend of people spending more on pet drugs before we got
married and invested. We've ridden it for well over 20 years...

2) Buy when the entire industry is a flaming wreck and there is blood in the
boardroom; assuming there is a good reason that demand for their products to
continue.

I've done this several times in natural resources (gold in the early 2000's,
oil in 2016) and it worked out decently.

3) One I haven't executed yet; certain firms are basically bets that a
specific event will occur, at which point the demand for their product will go
bananas. Buy in the quiet period and sell once the event happens.

Requires great patience to execute well.

------
SheinhardtWigCo
I wish it wasn’t this easy to get crypto-scam SEO articles to the front page
of Reddit and HN.

~~~
comatosesperrow
THANK YOU. I feel like I'm taking crazy pills watching everyone eat up the
article where HE ASKS FOR YOUR MONEY.

~~~
lucb1e
Wait, what did I miss? I thought the writing was bad (both contents and style)
so I didn't click through to the medium article, is that where they ask you to
invest in their stuff instead?

~~~
comatosesperrow
That's where it is. He then calls it a "set it and forget it" crypto bot. Yes,
please forget about the $1,000 you just gave me...

------
omarhaneef
How did you do 130 papers in 7 months? That’s just over a paper every 2 days.

What was the setup, how did you set up a pipeline? Was it R or Python? What
was the data source?

I am more surprised by your productivity than anything else.

~~~
bwilsonkey
I don’t think he actually did it. He linked to a crypto scam medium post
lol...

~~~
hobofan
> crypto scam medium post

Could you expand on that? I've only skimmed the article, but I don't see any
"crypto scams" pushed in the article. The article is just about something the
author seems to know about (algorithmic trading), applied to the
cryptocurrency market. It does promote the authors project (why else would you
write a Medium post), but in the worst case, that would be a normal scam and
not a crypto scam.

~~~
FiberBundle
The whole article just seems like an attempt to steal money from uninformed
people. He starts by giving vague information about trading strategies in
general, then linking to an article about Renaissance Technologies as an
example for successful algorithmic trading, then stating that most trading
bots aren't successful and that the crucial differentiator for deciding whose
bots to trust is the person's professional experience, which is obviously a
reasonable thing to do, however the picture at the beginning of the article of
him at a trading desk and him repeatedly mentioning his 7 year experience as a
trader combined with the complete lack of any actual proof that his bots are
actually profitable, make it seem as if he just tries to profit off his
previous experience.

He ends the article writing this:

"All you’ll need to get started is: 1\. $1000 2\. To press a single button to
get the bots started"

Furthermore in the reddit comments in response to the following question: "130
papers re-implemented in 7 months? I'm blown away. Write a software
engineering book about how you did it so quickly. Then write a self-help book
about having enough motivation to see it through." he writes:

"100-hour weeks and a desire for a better life for the ones you love will get
you there pretty quickly"

This guy seems like a complete fraud, I find it sort of sad that this has
landed on the frontpage of HN with that money upvotes.

~~~
hobofan
Oh don't get me wrong, I wouldn't trust the guy farther than I can throw him
either. I just wanted to be a bit pedantic about "normal fraud" vs. "crypto
fraud" (= ICO or similar).

------
rossdavidh
This is one of those cases where I would have guessed this would be the case,
but it's nice that somebody else spent their time to verify, since I'm
unwilling to spend my time to do so. Also nice that they shared their
experience with the rest of us.

If it worked, it wouldn't be published, or at least not until it stopped
working.

~~~
lucb1e
> Also nice that they shared their experience with the rest of us.

Except they didn't share the results with the rest of us. I know you said
"experience" not "results", but when disproving papers, the least you can do
is write down three sentences about each paper as you go along reproducing
them, noting what you are seeing, perhaps with a snapshot (just a zip file or
so) of the code. This is calling a whole field nonsense (that everyone expects
to be full of nonsense) without giving enough evidence for anyone else to
dispute your claims.

------
gillesjacobs
OP makes highly dubious claims and does not pass a basic smell test:

ALL CLAIMS MADE ARE DUBIOUS: Implementing 130+ papers in 7 months is highly
implausible. This would require:

\- Insane productivity.

\- Implausible access to pricing and news data resources (which are often not
freely available).

\- Expertise in machine learning, natural language processing, finance, and
data science. OP had to implement financial, time-series and linguistic
feature engineering pipelines, as well infer the architecture and hyper-
parameters used in all papers AND train all these models. ALL WITHIN 7 MONTHS.
All while previously being a trader professionally i.e. not likely an expert
in many of these fields.

\- OP also claims he "web scraped" all the data which is highly unlikely as
price datasets are often sold for a pretty penny and not publicly available in
the detail described in several of these papers.

\- Down the thread, OP says he does not know what a "meta analysis" study is
al the while being capable of implementing 130+ papers. So someone who is an
expert in ML, statistics, data science and finance does not know one of the
most basic types of scientific study. All the while essentially engaging in a
meta analysis study.

\- OP describes himself as "a trader at a Tier 1 US bank" to lend credibility
to his post: in itself that description is ridiculous and sounds like a naive
attempt at instilling authority.

\- When others encourage OP to publish results, he answers evasively:
"probably a bit deep for a public forum but I was kinda glad to see the back
of that work. It was an awesome learning experience but it's pretty soul
destroying experimenting with tonnes of stuff that just doesn't work."

EVIDENCE PROVIDED: Non-existent.

All OP has to show for all this work is a hastily written Reddit post with
dubious claims. There is no proof of the work done whatsoever, no code
samples, not even result tables or graphs. The discussion of basic results are
often made criticisms of this line of research.

MOTIVATION:

At the end OP shills his cryptotrading bot. This post was likely all just
purely made-up to market his cryptotrading bot service. OP uses some common
criticisms of market prediction research to garner authority as a wizz-kid to
attract people to his crypto scam.

What's worse many on HN and Reddit seem to gobble it up naively. Seemingly
because OP is critical of something that is popular to criticize.

------
tripzilch
I read the post and .. he basically _said_ he did it, and just claims the
results (that are so tasty because they fit everyone's preconceptions). And
that's all the information there is. _Not even listing the papers that he
supposedly reproduced._

And then at the end of the post, you get a link to a blog post about crypto
trading bots, which isn't even relevant in context, starts out reading like a
intro/tutorial but it ends with "all you need to get started is $1000 and to
press this button" ...

Upvoted all the way to the front page? What the hell, HN. Flagged.

------
whoisninja
more important than this study if done correctly is the fact that he built a
framework that can ingest all this data and that he had access to all these
datasets

typical hedge fund spends millions of dollars in order to build such
frameworks and buying datasets, sure most academic papers fail if you
replicate but the framework and datasets are very valuable because you can
eventually find something on your own or an improvement on existing ideas if
you keep trying hard enough + there are other sources of ideas like quant
research from brokers, ideas from platforms like quantopian etc. but yea in
general if you have an outstanding idea that works - you would have very less
or no incentive to publish it. why would jim simons have his researchers
publish anything when they can make money for him all day long everyday ...
just my 2 cents.

~~~
whoisninja
i asked the author where did he source his data, his reply was "i scraped
data"

how can you scrape pricing data? not every data in this is on public domain,
otherwise there would be no Bloombergs, CapitalIQs selling data for
millions(sure they're overrated and overpriced but still!). Or in other words,
if he is right - he can sell data and make millions. no need of looking for an
investment strategy. just my skeptical side saying :)

you need clean data to accurately test ideas. for instance getting tick data
is quite expensive. most universities have free access to Bloomberg, CapitalIQ
etc. datasets the reason professors can test and also the reason some smart
guys in the industry work for university on the side

~~~
lettergram
You can get data for free from places such as alpha vantage

[https://www.alphavantage.co/](https://www.alphavantage.co/)

~~~
whoisninja
it's not enough data to test all of those 130 papers, he said he has also
tested short term reversion trends. for that you need tick data(or atleast
minute/hourly data)

~~~
bwilsonkey
Agreed — something about this post doesn’t pass the smell test.

1\. He’s a profitable trader at a tier 1 firm who has the spare time to not
only develop a series of algorithms based on 130 research papers, but also
sufficiently backtest them in 7 months?

2\. He said he looked at the past 8 years of papers, but refers to multiple
models correctly predicting the 2008 financial crisis.

3\. Where are the code samples?

Edit:

Lol, just realized his medium post ends with a crypto scam.

~~~
hihi123
Ditto on the "130 papers in 7 months." I am not familiar with the field, but I
assume the process would look like this:

* Read and understand paper

* Find and download appropriate input data

* Code paper model and validate (he said he wrote his own code)

I can see myself being able to do this for ONE paper in maybe a week. He
claims he was doing 1-2 of these per day. Wow. So either there is some
exaggeration on his part, or he is a total wizard in his field.

~~~
sgt101
I think you are a quick study; typically it takes me a week to figure out the
detail of what's being done in a paper. Getting the data and testing would
take longer. Charitably he/she has a framework with all the data required
sitting ready to go and is just writing wrappers to the models. But even
downloading frameworks from Github and getting them working takes a couple of
days - for me. For example I've been playing with the Graph-network code from
deepmind for a few weeks - I had to learn how the graphs were represented, how
to build them and access them and how the models were made and put together.
Just working that out was a solid three day job. Now I can build things and
test out what's going on in the examples and get a feel for the framework,
probably (if there was a problem) I would be in a reasonable position to say
"this doesn't work like they think it does" (it does, but no surprise) but
unless you've done that leg work I think you can't really. I think a proper
replication effort is really 1 man month of expert time - or really you're
just throwing stones.

------
ddxxdd
>Literally every single paper was either p-hacked, overfit, or a subsample of
favourable data was selected

including methods that use:

>News Text Mining. - This is where they'd use NLP on headlines or the body of
news as a signal.

I have to call this out.

Is this author suggesting that you couldn't have made money by shorting Enron
stocks milliseconds after the scandal was made public? Is it impossible to
make money by buying a stock in a small company, seconds after an acquisition
is announced? If a CEO gets sent to prison, will that company's stocks not be
affected?

And then there are other methods that use:

>Fundamental data. So ratios from the income statement/balance sheet

So buying stocks in companies with good financial health is not profitable?

Something's being left out here.

~~~
mediaman
The important concept to understand about profitable investing is that you
have to have a strategy that others are not also using.

Sure, investing in companies in good financial health is profitable. Unless
everyone else does it too, and they drive up the price of the profitable
companies, until all upside is gone (i.e., price is baked in). You're not
better at finding profitable companies than anyone else.

Shorting stock on headlines? Sure, if you can beat everyone else. (You can't.)

The other is merely stating that, according to his analysis, apparently all
these strategies did not bring an edge to the market.

~~~
rocqua
> The important concept to understand about profitable investing is that you
> have to have a strategy that others are not also using.

Not quite, because stocks tend to go up.

What really is hard is making more profit than just holding stocks would.
Because that takes actual new ideas.

~~~
grenoire
Stocks do not tend to go up, that would imply a greater than 0 return average
for series. We instead get mean blur and skewness (which is actually often to
the left).

The aggregate of the traded stocks, i.e. the market, goes up on average.
That's why you make money by holding diversified portfolios.

------
ricklamers
I think this is not rigorous enough to draw any real conclusions.

If he had done a proper job of reproducing he would have created a write-up of
his work explaining his reproduction methodology. The next step would be to
get his work peer reviewed.

I think only then you have come close to the amount of analysis and rigour
necessary to discredit so many authors of (possibly peer reviewed scientific
articles) academic research.

The fact that he mentions that he doesn't know what a meta analysis is in the
comments suggests that possibly _his_ results might not be what he purports
them to be.

~~~
gillesjacobs
Exactly, I also find it extremely dubious a "Tier 1 professional quant trader"
implemented 130+ papers in 7 months.

This means obtaining the same data, reproducing feature engineering and
hyperparameters. Implementing learning algos. Maybe the guy is genius and god-
like in NLP, finance, data science and machine learning but even then 7 months
is too little time.

I was amazed at how few people call out this obvious lie here.

------
zazagura
Some of the papers I've seen are ridiculously obviously over-fitted.

For example published in 2018, but "tested" on 3 months of 2010 prices of
GBP/USD, USD/SEK and USD/THB. Quality forex data is so easy to get freely,
that picking 3 months from 8 years ago on one major pair and two other random
minor ones just stinks.

~~~
quirmian
Where can I get good quality forex data from?

~~~
capnrefsmmat
I used TrueFX for an assignment in a statistics-for-finance class I taught:
[https://www.truefx.com/](https://www.truefx.com/)

------
jmpman
Has anyone tried a simple approach - trying to predict which of the S&P 500
will have the lowest 10% returns, and build an (S&P 500 - 10%) index? It seems
obvious that the S&P is stacked with some great companies and some old dogs.
Does that method not work?

~~~
smnrchrds
Since it's obvious, everyone knows it. And since everyone knows it, it's
already priced in. You cannot find an edge by acting on widely-known public
information.

~~~
jmpman
I’m acting on the fact that almost all my investments are in the S&P 500, and
I don’t have a (easy) way to pick my favorite 450 out of those 500. How would
knowing the worst 50 be already priced in? They would be priced lower than
they should? Good let’s get them out of my index. How else?

~~~
nostrademons
They'd be priced low. There is no "should" when it comes to markets - the
market price is whatever people will transact at.

The problem is that stock price movements depend upon _future_ events - making
money in the markets is effectively a future-prediction problem. So if your
strategy is to discard the bottom 10%, great, you got rid of Foot Locker and
Sears. You also would've gotten rid of Apple in 1998, which was responsible
for a good portion of the index's gains over the last 20 years. And you
would've kept losers like PG&E, which went bankrupt over a black-swan event
(they were doing fine until they burned down a town).

------
xondono
Everyone can drive a car in a straight line looking into the back mirror.
Trouble only starts on the first turn.

------
leet_thow
The stock market is a complex adaptive system where the agents are constantly
changing their strategies so that even if you were to find inefficiencies or
patterns, they are only ephemeral.

~~~
deepnotderp
This is why the truly successful quant groups like Renaissance continuously
adjust their strategies and come up with new ones. Renaissance in particular
has invested heavily into their data processing pipeline which enables them to
have a significant advantage over the rest of the field.

~~~
leet_thow
Yes, if I remember correctly from an interview I saw with the founder he
mentioned that the barrier to entry used to be high and that commodities
markets 'used to' trend.

------
paultopia
Interesting but it would be nice if the author would, you know, write up
his/her own detailed analysis with replication steps and post on arxiv or
something.

~~~
defertoreptar
I got the feeling this was more of a case of "I did all this work for myself.
Nothing useful came up, so here's what I found." The author may be ok with
spending an hour sharing the findings, but doesn't want to spend more time
than that.

~~~
numakerg
>Nothing useful came up,

Except plenty useful came up. "Literally every single paper was either
p-hacked, overfit, or a subsample of favourable data was selected" out of 130+
is a significant result. The media would jump on this, and they just might
even without any proof of work.

~~~
defertoreptar
I was referring to the word "useful" within the context of the author's
hypothetical goal, not the parent poster's (i.e. a strategy useful for making
money opposed to sharing knowledge).

------
whoisninja
the author is trying to sell crypto trading bot ! looks shady to me if
anything guarantees you profit, run away from it as fast as you can :
[https://credium.io/](https://credium.io/)

[https://towardsdatascience.com/crypto-trading-bots-a-
helpful...](https://towardsdatascience.com/crypto-trading-bots-a-helpful-
guide-for-
beginners-60decb40e434?source=friends_link&sk=6c68390cbf6ae7f464ad1666d55dba4b)

------
linux_devil
"The most frustrating paper:

I have true hate for the authors of this paper: "A deep learning framework for
financial time series using stacked autoencoders and long-short term memory".
Probably the most complex AND vague in terms of methodology and after weeks
trying to reproduce their results (and failing) I figured out that they were
leaking future data into their training set (this also happens more than you'd
think)."

\- Not sure how author tried to implement it , but is this not how you train
LSTM networks by feeding t+1 data back into the cell again to predict t+2
data. It will be easier if author made it open source as well

~~~
laichzeit0
Leaking future data in would be using t+1 for t, e.g. something like a bi-
directional LSTM. I assume he means the actual training dataset had some kind
of signal in the data that was also in the test data.

~~~
sgt101
People do this by doing things like testing that their features contain
information in both the training and test set. Because they are not exposing
the data directly to the classifier they think that they haven't compromised
the test set - but what they have done is increased the chances of a chance
correlation.

------
WheelsAtLarge
I could bet a ton that most people will make excuses as to why the papers
failed. There's something within us that wants to hit the stock market
lottery.

I truly believe that there are streeks to profits in the stock market in the
same way you will find streeks in any set of random numbers but they are
impossible to find in a consistent manner. The road to wealth for most in the
stock market is time and investing in a basket of good stocks.

Whoever thinks that they have found a system to profits in the stock market.
Test and retest your method a few times. It's unlikely you have a winning
system.

~~~
bjourne
But there is a foolproof way of profiting from the stock market. Insider
trading! I find it fascinating that people do not believe it occurs at a grand
scale given the low risks and huge rewards. Exactly like how people believe
athletes don't use steroids so they get all upset when every once in a while
one is caught. :)

~~~
saalweachter
One of my favorite not-quite-conspiracy-theories is that all of quants and
crazy trading algorithms are just to provide cover for the insider trading.

------
scarmig
Wouldn't any given approach rapidly lose efficacy as soon as its published?

I would even guess that a paper being published means that, at the point the
paper started to be written, its alpha had already decreased to zero.
Otherwise the writers of the paper would still be using that approach. That's
how it can appear to provide no value even if you extrapolate it back in time.

~~~
SubiculumCode
The author mentions this, and said he tested for "alpha decay" by applying
method to datasets that preceded the data on which the model was
tested/trained.

------
grahamannett
I barely skimmed this
[https://journals.plos.org/plosone/article?id=10.1371/journal...](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180944)
for the "most frustrating paper" but how did he determine that they were
leaking "future data"?

------
riku_iki
TLDR, no results, no code, no details, just admission of failure from
anonymous redditor with SEO link on his unrelated crypto-trading project.

~~~
mxcrossb
It’s baffling to me that anyone would believe this post. Do people here not
have an ounce of skepticism?

~~~
javert
I think people aren't skeptical about this because it lines up with what they
already expect. That's the case for me.

If the guy is lying and it's all made up, I'd _still_ wager that if you
assembled a collection of 130 papers on this topic, at most 10% would be valid
[1]. So even if he's wrong, he's probably not wrong.

[1] I have an academic background, and I think invalid papers make it through
peer review constantly. Peer review is not what people think it is.

------
RickJWagner
I tend to follow Jack Bogle's advice. Jack made many people wealthy.

His advice? "Nobody knows nothing." (Read Bogleheads.org to see how to
leverage this into wealth.)

------
zallarak
Some of the smartest people I've ever known from college became professional
stock market investors and traders. Not a single one of them has significantly
outperformed the market.

I think of stock market investing as a white collar gig economy job. Both
depend on some central entity, which makes a killing (brokerages in this
case), and have extremely low barriers to entry.

------
bitL
Hasn't there recently been shown that only strategies using simple momentum-
derived technical indicators were able to consistently bring returns in the
stock market?

~~~
DennisP
Not just recently, it's been known for a while. The author actually confirmed
it:

> Almost every instrument is mean-reverting on short timelines and trending on
> longer timelines. This has held true across most of the data that I tested.

(But momentum isn't the only thing that appears to work.)

------
m3kw9
He didn’t state the time frame things are predicting. Going long is still the
best way to beat any hedge funds or “pros”

------
maremp
Source OP deleted the post. Is there a mirror?

------
kart23
Buy low, sell high. Focus on reliable, proven stocks. It's not hard. Buy on
bad days, sell on good days. Dont overcomplicate things.

------
unnouinceput
To OP (janny_kul@HN and chiefkul@RD): "Show me the code! Show me the code" \-
in obvious Cuba Gooding Jr. voice

Until then I call this a fraud

------
SirSavary
Disappointed that this made it to the front-page of HN. Had it show up in my
push feed because it was so popular; quite upset to click it and see the
comments in here falling for someone who's a crypto scammer:

\- Redditor for 28 days; first post made to /r/investing and contained self-
promotion for a website that functions as a search engine for stock market
fundamentals (post removed by moderators, take a look using
[https://ceddit.com](https://ceddit.com))

\- Has made a few, low impression comments across other trading subreddits
(/r/algotrading and /r/securityanalysis)

\- Claims to have been a trader at "multiple Tier-1 US banks" for 7 years but
a glance at their LinkedIn shows less than five and a half years (65 months)
between two organizations

\- Claims that they found, analyzed, and reproduced "130+" academic papers in
only "7 months". Even by superhuman standards, the outlook for completing this
much work (at high quality) is grim. Here's some napkin math:

7 months * 30 days = 210 days 16 hours (superhuman) * 210 days = 3,360 hours
3,360 hours / 130 papers to reproduce = 25.8hrs per paper

These numbers are intentionally rough to show that even if we're being
generous, the idea that any one person could somehow recreate a full academic
paper in less than two 16 hour working days is absurd. Add to this OP's claim
of spending "weeks trying to reproduce [a paper's] results" and it the task
becomes even more daunting.

This is possibly the biggest red flag of all -- or OP is the world's first
(and only) 100x developer.

\- Does not provide any empirical data to back their claims, only provides the
name (not a link) of one paper, and when asked in comments to provide more
information -- fails to deliver.

This post desperately wants to come off as a research article but it's missing
all the fundamentals to make it so. If OP's claims were true, why wouldn't
they post their raw findings? We could make that argument that there's far too
much to post, 130 papers would be a good bit of code, but there's no reason
that OP couldn't provide a listing of the papers they "reproduced" at the bare
minimum.

OP has made extraordinary claims: most if not all "predicting the stock
market" papers are fraudulent, but has failed to provide any supporting
evidence to back this up beyond their own words. As someone who just analyzed
well over a hundred papers and postures themselves as a data scientist, OP
should know that citing yourself doesn't fly in this scenario.

\- OP ends their post with the following: "I try to write a bit on medium even
though I'm not a great writer if you wanted to read more from me."

There's absolutely nothing wrong with self-promotion, provided you're a
quality creator and are transparent about what is being promoted. OP is
neither of these things.

Clicking the Medium.com link will take you to a blog post titled "Crypto
Trading Bots · A helpful guide for beginners [2019]". If you're like me, you
might assume this is a tutorial related to crypto trading bots. Perhaps
information on setting one up, coding one yourself, or an overview of the
landscape. It is none of these things.

Much of the article explains how cryptocurrency trading bots work -- which is
great, but quickly goes down the path of telling the reader that most bots are
garbage and won't return a profit. Near the end of the post we're advised how
to choose a viable trading bot and are provided with three questions to ask
ourselves:

1\. What is the professional experience level of the senior leaders of that
firm? 2\. Are their algorithms widely known and openly available to anyone?
3\. Is their success aligned with your success?

Immediately after these questions are the following lines: "Unfortunately,
choosing a trading bot to go with isn’t as trivial as answering these three
questions. In my opinion, everything ultimately comes down to people."

What people, you ask? Perhaps someone like OP, who happens to be the founder
of a trading bot platform. The post finishes off with a not-so-subtle advert
for his company, along with the extraordinary claim that it will "take full
responsibility for the profitability of our clients". According to the OP, all
you need to get rolling on his platform is "$1000" and "to press a single
button to get the bots started", never mind that the platform hasn't launched
and AFAIK there's no start button to press.

Clicking through to the platform's website will bring you to a scroll-jacked
landing page full of marketing fluff. Scroll further down (or click the "Get
Started" button) and you'll see a pricing table with only one option currently
available: pre-order a "$145 single fee lifetime license". Compared to the two
unavailable plans, $145 is a steal -- the next plan down would run you $660
($55*12) a year. Combine this with the "First 90 days profitability or money
back guarantee" and the whole damn thing sounds like an incredible deal. But
you better act fast because this offer is only available to the "first 1000
members".

OP's other trading bot articles aren't much better and in my opinion, directly
promote his platform.

~~~

The whole thing sets off numerous alarm bells in my head -- and it should for
you too.

A trader who worked at "Tier 1 US banks" should know that guaranteeing the
profits on your first 1,000 customers is not only ridiculous but so ambiguous
as to be useless. Every developer on this site should know that not a single
one of us would be remotely capable of maintaining a death-march level working
pace for 7 months, launching a (credible) startup, followed by making non-
zalgoized Reddit posts lacking any reference to the void.

OP isn't an OG superhuman developer who worked for a bunch of big banks and
learned all the secrets. They're a former trader turned wantrepreneur that's
resorted to dirty tactics to promote their venture. As far as I'm concerned,
and maybe it's inflammatory, but OP is a liar. Nothing more.

------
throwaway156503
Technical analysis is pseudoscience.

------
gingabriska
Let's say an event occurs and you know a particular stock will go up 20% but
you pump enough money into the stock to make the stock go 30% and then you let
others chase the stock and publish news about this market move through media
houses / content spamming / fake accounts. Then you short the stock once you
get the desired movement and finally you remove the money from this stock so
it goes into free fall and all people start selling. Finally, you close the
short position when you notice there is no room for stock going further down.
Now let's say stock ended up at 10% so you buy more stock so it goes 15% just
below what you initially predicted.

Assuming you've billions to move the stock.

Why such strategy will not work?

~~~
anastalaz
Such a strategy does work and it's also illegal. It is called market
manipulation specifically 'pump and dump'
[https://en.wikipedia.org/wiki/Market_manipulation](https://en.wikipedia.org/wiki/Market_manipulation)

------
rolltiide
yeah but you can make a lot of money preaching technical analysis to your
congregation a few times per week

protip: a fibonacci retracement from a randomly selected extreme will always
tell you something

protip: it takes 5 months for your congregation’s account to get eaten up from
transaction costs when their stop limits keep getting hit

in the mean time you can just play TA roulette and they’ll always be impressed
by your “uncanny” perceptive abilities

~~~
throwawaywego
If you know that 5.6% of the users (automated, or parroting the preacher) of
an exchange will use fibonacci retracements, and that 15% of the amateur
market will follow the market price change caused by either buying or selling
activity, then you can play roulette with a decent edge. Of course, not as
much as the preacher, who is allowed to bet before (s)he will speak to his or
her congregation.

When you gather enough of these commonly used technical analysis, it's like
having to predict in which startup Ron Conway will invest, but you can
calculate Conway in a Python one-liner, and keep up-to-date by going to weekly
sermon.

~~~
rolltiide
yes, this is possible, with more trades becoming a self fulfilling prophecy,
and have stop limits in place all the times it didn't work.

I wish the technical analysis flock would merely incorporate different things.
TA takes a time series chart and imagines time series patterns. It primarily
neglects what didn't get printed on a time series chart, and why. How big are
the orders at the resistance level, do you have a record of the order sizes
that appeared at the last resistance level? who is selling at the resistance
level and why? The TA answer is "just because its a psychologically round
number for the resistance price" or "because thats how high the last high
candle was", but you can greatly improve your win rate by understanding who is
in the market and why, which is possible to understand and a large portion of
my trading strategies. It can be much more data intensive though so I can see
why 1980s gurus did not do it.

------
paulpauper
_Literally every single paper was either p-hacked, overfit, or a subsample of
favourable data was selected (I guess ultimately they 're all the same thing
but still) OR a few may have had a smidge of Alpha but as soon as you add
transaction costs it all disappears._

I could have told you that without testing. If anyone had a lucrative strategy
would they disclose it in a paper to the general public? I think not.

~~~
lucasmullens
Proof can be more useful than a hunch.

~~~
numakerg
And the author has shown none.

------
lidHanteyk
This is unsurprising. P likely is not equivalent to NP [0], and predicting the
market is NP-hard [1]. It's nice to see empirical work in the field, though,
and especially nice to see reproductions of published papers.

[0]
[https://www.scottaaronson.com/papers/pnp.pdf](https://www.scottaaronson.com/papers/pnp.pdf)

[1] [https://arxiv.org/abs/1002.2284](https://arxiv.org/abs/1002.2284)

Edit for downvoters and repliers: If enough market participants are
irrational, then it can still be possible for people to predict _other people_
, instead of the market, and make money that way.

NP-hardness indeed doesn't rule out heuristic approaches, but experience with
3-SAT and other NP-complete problems suggest that there will be arbitrarily
bad times, and that in those times, the amount of loss can be exponential in
the length of time that the heuristic poorly predicts the market.

~~~
dvt
Philip Maymin seems like a serious guy... but that EMH ↔ P=NP paper is
absolutely not even remotely a _proof_. Was genuinely very curious and it's at
best an intuition. Some claims, e.g. Knapsack and 3SAT are (almost?)
isomorphic to the efficient market hypothesis, are pretty bold. And the
justification is hand-wavy at best.

~~~
pzone
He's not a serious guy at all, he's a nutjob who likes listening to himself
talk.

~~~
pmaymin
Why must serious guy and nut job be my only two options. And why must they be
mutually exclusive.

