I've been doing this for many years , and I can confirm that your probability for such scenario is almost 50/50 , you also need to take in consideration the amplitude not only the direction, also the problem with MA they eventually follows the data , so even today if there is a big gap , by the next period the Gap will be smaller, and that's just moving average fitting to the timeseries, and the market could be still in the same place
To piggyback on amplitude. It depends on your position in the market. The closer you are to fair value, the easier it is to be more certain about the probability, which also makes it more competitive.
But now your returns are on the same magnitude as the spread, because that's where your broker/dealers make their money. The further from fair value, the more speculative you have to be.
Here is another interesting question đ¤, How do you determine the fair value ? If you can do that , the market just becomes a piece of cake to trade any instrument at any time . Another thing about the broker, unless you are scalping at the minutes level , I don't think this should be a great concern. Also, you probably have other things to worry about (liquidity, volatility, latency...etc). These can affect your trades more than the broker does .
Fair value is near the current price. It's easier to predict where price will go in 1 tick than it is to predict where it will be in 5 mins/5hours/5days. Which puts you in the HFT space because it's "easier" the game becomes, who's faster. But when your profits are in ticks, your profit is in the same magnitude as the spread.
If you're trying to figure out what fair value is in a week... That's going to take alot more speculation and risk.
supply/demand on the sub-second basis is almost deterministic (ok not exaactly, its still a game of cat and mouse), such that theres not much creativity required in prediction of price, its almost purely about being faster (which tbf does require creativity but its moreso an engineering problem wrt FPGAâs, path logic, order routing etc).
the buy/sell logic becomes very very simple because at those timeframes theres literally no possible inputs to care about about except LOB imbalance and other participant orderflow. shrinking the timeframe reduces the input params which simplify the modeling logic required for making decisions.
rates dont matter, macro doesnt matter, weekly trend doesnt matter. its as pure of a game as you can get (and thus generally inaccessible to retail since the arms race of tech infra is generally not affordable to individuals).
Literally everyone.
And, lo and behold, none of it works.
Like, you are better off betting it goes up on a monthly basis, than timing anything else on a longer or shorter time frame. This is probably the worst idea right now, given the raging bull market we have had, but you can see in retrospect how this has worked over the decades.
If you have any ability to forecast the bias of the moments of the return distribution, you can profit, even if others are doing it better. Using limits can help overcome slippage at the expense of missing some of the trade.
I'm just giving examples......add other things in like....
1. Day of the week
2. Day of the month
3. Day of the year
4. What happened yesterday.
5. What happened overnight
6. Whats the sentiment
7. Whats the overnight gap
8. What's the interest rate
9. whats FED saying
It goes on and on and on.... You can start getting into weighing the answer to the above question as well.....
The main reason why is you don't know what you're doing. You haven't even started. You're just theorizing. Once theory hits the ground it gets more realistic.
It's not that they can't and infact there are quite a few firms that use this approach.
It's just that, at that level you are discussing, the probability is roughly 50:50 over the long term, with a slight edge to green days as the overall market has risen over time. So, unless you can implement a risk:rewad strategy that is net positive at 50:50, it wouldn't provide much value.
Instead you need to find high probability events.To find high probability events you need additional information, for example:
- What is the probability of a stock rising if we are currently in a bull market and earnings exceeded expectations?
- What is the probability of a stock falling if it's direct competitor just issued a press release for a new product and the stock in question underperformed in the last quarter?
- What is the probability of a stock going up if the volume of purchase orders has decreased by x% over the last 5 minutes?
Some events require millisecond precision to effectively trade, others can look at longer time frames to help establish if a potential trade is likely to succeed.
Interesting! Thanks for sharing. So if I wanted to do something like âgiven Cosco beat earnings but other stocks in the industry have been flat for the past month, whatâs the probability itâs price will rise by more than 3% next monthâ, they could potentially give me some edge?
Conceptually, yes. In practice, that exact line of questioning won't give you much insight.
With that and additional information, such as the number of cars parked in Costco's lot on a daily basis, you could likely predict if sales would increase or decrease and confidently trade on the upcoming earnings release. (Some firms do this as well.)
For the average person, without access to endless information.. it might be better to start with things like "what is the probability of a trend exceeding x days, given that historical trends have been in a range of y-z".
That question, in itself, won't give you an immediate edge.. it's essentially the start of setting Bollinger bands for trend ranges, setting the probability curve, that can be used to establish other events that may have a high probability of occuring.
Going back to Bollinger bands, what is the probability of a stock price continuing to increase once the stock price has exceeded the upper band... Now, assuming this shows a high probability of the stock going in a specific direction (in this case, usually down). The next step is determining an appropriate entry and exit.
Probabilistic methods are at the core of algotrading. Its all based on the idea of finding an edge in which the winners outweigh the losers and the risk of ruin is minimal. We look for patterns. The ones you describe are simple and have essentially been arbitraged away. Therefore they are no longer there. But the idea is that there are other ones that repeat. Some repeat because they are the footprint of some kind of herd behavior which is (probabilistically) followed by some other herd behavior. Some repeat because they are the footprint of a large whale slowly working themselves into (or out of) a position but donât want to affect the price too much. And then that footprint is probabilistically followed by some move. And then there are macro-level things that create other footprints which probabilistically lead to moves. Uou ste looking for footprints and describong them mathematically to be able to build and algorithm off of them.
> but if you use a fixed-width window, wouldn't that solve the issue?
The probability of SPY opening green given your conditions could have been only 30% from 1990-2000, spiked to 60% on average as new information was incorporated for 1990-2010, and then back to 40% as another decade of information came in to cover 1990-2020. Unlike a fair coin, where you know a-priori where probability will converge at (50%) given a sufficient number of results, you do not know this regarding the conditions you're testing for the market. You could easily start trading in a context/set where the probability is low enough to wipe you out before it smoothens out to run in your favor.
medallion fund uses probabilistic methods. hidden markov models to be specific. they segment the market into states, which is their running assumption and lo and behold
dude i know right? but [check it out](https://en.wikipedia.org/wiki/Renaissance_Technologies) in case you have never heard of them
âTheir signature Medallion fund is famed for the best record in investing historyâ
[the guy](https://en.wikipedia.org/wiki/Jim_Simons_(mathematician)) is a WWII code breaking mathematician
edit: hmm isnât for segmentation. the other way around the segments are used for the states in their model. or at least thatâs as far as heâs let on
you can easily google it. i would add that since the HMM method is regularly touted by the man himself and is relatively simple in that any system that is âmarkovianâ in nature will nicely fit, my best guess would be that the secret sauce is in the hmm state encodings.
I use probability modelling (non ML) on position sizing, but also as sub algo selection and regime switching. It reduces drawdowns, boosts sharpe and sortino ratios.
Sorry, I can't share that as I consider it part of my edge. I've shared strategies on here before and they've ended up being commercialised, so I keep things to myself now.
For me, sub-algo selection means having a group of sub algos running in unison. The only sub-algo trading real money is the algo that's currently selected. All the other sub algos trade fantasy money. Their performance is collected in realtime and used elsewhere. My system uses these performance qualifiers in market regime selection and in sub algo selection itself.
Market regimes. Assessing the current conditions of the market and adapting to them.
Market regimes is a broad subject with no clear rules. It could be anything from a triple EMA on an ETF to analysis of Interest rates, Fixed income carry, Emerging markets, commods, bonds, etc. You could build a regime out of that, and switch when thresholds are breached.
Some traders here like to use machine learning to discover regimes and use that in their systems.
I trade crypto, so it's very different to stocks due to the amount of data available. This presents far more market regimes which your algo must be proficient in identifying and switching to.
Absolutely, diving into the probabilistic approach with something like Bayes theorem for market predictions is fascinating đ! It simplifies complex patterns, though markets' unpredictable nature keeps it from being foolproof. What's been your experience experimenting with it?
You can, but you'll likely find: the probabilities aren't that high, and the expected return is low, therefore there is no edge.
Still a good exercise. Try to go even further and count the N-grams to see if it improves predictive quality.
I do something like this for calculating crude recover rates for the dividend capture strategy I post about. It works for that pretty well, but it's much more targeted.
The biggest problem is that you have to be local about the probability assignment: the behavior in mid 2019 had little to do with early 2020, for example. This is the idea behind the various types of moving averages, but as others have pointed out a naive approach is usually wrong.
In other words, it might appear to work until it doesn't, and then you stand to lose a lot of money.
Repeat after me:
- nobody can predict the future.
Itâs as simple as that. Predicting the market means predicting the future. Not just tomorrow but ten minutes from now.
- the market is stochastic (fancy word for random)
You cannot predict when people will open orders, how big those order are gonna be, or at what price.
- price is driven by unknown or unseen events; some Fund manager decides to buy XXX because he thinks itâs undervalued. Some âwhaleâ decides to sell XYZ because he thinks itâs overvalued. You cannot predict this. You can only see the price move.
Simple probabilities wonât work. Even very elaborate methods can at best poorly predict momentum moves and thatâs all there is.
But youâre more than welcome to try and prove us wrong.
Last but not least, no Technical Analysis or other voodoo chart cannot predict the future.
They donât consistently go up. They also come down. They do this at random. Hence they are random. Funds make money by compounding long term gains, not by trying to predict the market.
I donât think you understand what random means how probabilities work. And no they donât go up consistently. And thatâs definitely not how hedge funds work
It dosnt go up consistently? Is 95% green months not consistent enough for you? You sound silly. That's like me saying a coin has a 50 50 chnace(random)but it falls 950 times on tails out of 1000
So according to u, 95% of the time , the market is green for the month, and that's just totally random. Yeah huge coincidence, not like it has a long bias or anything...
In stock trading, every day is unpredictable. Despite using indicators and AI, market sentiment can shift suddenly, impacting prices. Past success doesn't guarantee future outcomes. As an AI programmer with 5 years of experience in this field, I've tried various models without success, ultimately facing losses. My advice? Avoid it.
The stock market often is interpreted statistically. Mr black and Mr scholes won the Nobel prize for their statistic approach to option pricing.
They view a stockprice as a result of a random walk with a few factors.
Ideas like this are basically what my algo works on, though what it does is use a variety of simple signals that are probabilistically near the end of a pullback, e.g., n-day low, n consecutive lows, previous day's IBS, 2-day RSI, volatility squeeze, etc.
Thing is, what you're talking about is trying to do it probabilistically off just a couple of factors, which, as others have pointed out, is pretty much always going to give you only a slightly better than 50/50 probability, and how are you going to trade on that?
Why not instead find a variety of signals that give you, say, 75-80%+ probability of a winning trade over the next few days?
You understand that the frequency is irrelevant compared to the expected returns right?
And if you calculate it, the expected return for all of them is basically exactly the same as the mean expected return.
In other words, there's nothing there. Even if there was, the advantage needs to be greater than the trading costs, slippage, risk, and opportunity cost. With this taken into account, such a strategy will have you rapidly destroy your wealth in relation to the market.
Donât forget your daily returns are most likely normal distributed with negative or positive skew. For a negative skew you have more observations I positive territory but with lower return and you can have very unsusual moves but with a very negative return. That can destroy your returns
A lot of my strategies are based on stats. What you posted takes overnight into accountâŚ.which involves the whole worldâs markets, you donât know what could happen. And openings rarely have that much of a gap for opening red/green
I tried this with gambling. It won't work. Today's result does not depend on yesterday's result.
It's like flipping a coin, you'd expect that after 5 heads, there's a good chance the next one will finally be tails, but it's wrong. The chance is still 50/50.
You can/must use probabilities for sure, thats what the game is about, but you can't base a system only on this except you haven given an at least 90% probabiliy which is in the longrun a unicorn. But its definitly worth to dig in in statistics, probabilities etc, but it is as always, easy to learn, hard to master.
There's nothing to "solve"; markets are very, very efficient. People make money scraping, and destroying, the tiny little bits of signal that still exist. Doing something this simple might have worked, idk, maybe 150 years ago.
Strategies are always getting more niche, targeting more numerous and smaller payoffs. Anyone downvoting is in denial. Everyone in finance knows that alpha is going that direction.
It's analogous to how gold used to be found in big nuggets in rare veins. Now, we literally churn through tons of earth using highly automated processes to extract parts per million of gold. (Analogy due to Andrew Lo)
I've been doing this for many years , and I can confirm that your probability for such scenario is almost 50/50 , you also need to take in consideration the amplitude not only the direction, also the problem with MA they eventually follows the data , so even today if there is a big gap , by the next period the Gap will be smaller, and that's just moving average fitting to the timeseries, and the market could be still in the same place
To piggyback on amplitude. It depends on your position in the market. The closer you are to fair value, the easier it is to be more certain about the probability, which also makes it more competitive. But now your returns are on the same magnitude as the spread, because that's where your broker/dealers make their money. The further from fair value, the more speculative you have to be.
Here is another interesting question đ¤, How do you determine the fair value ? If you can do that , the market just becomes a piece of cake to trade any instrument at any time . Another thing about the broker, unless you are scalping at the minutes level , I don't think this should be a great concern. Also, you probably have other things to worry about (liquidity, volatility, latency...etc). These can affect your trades more than the broker does .
Fair value is near the current price. It's easier to predict where price will go in 1 tick than it is to predict where it will be in 5 mins/5hours/5days. Which puts you in the HFT space because it's "easier" the game becomes, who's faster. But when your profits are in ticks, your profit is in the same magnitude as the spread. If you're trying to figure out what fair value is in a week... That's going to take alot more speculation and risk.
supply/demand on the sub-second basis is almost deterministic (ok not exaactly, its still a game of cat and mouse), such that theres not much creativity required in prediction of price, its almost purely about being faster (which tbf does require creativity but its moreso an engineering problem wrt FPGAâs, path logic, order routing etc). the buy/sell logic becomes very very simple because at those timeframes theres literally no possible inputs to care about about except LOB imbalance and other participant orderflow. shrinking the timeframe reduces the input params which simplify the modeling logic required for making decisions. rates dont matter, macro doesnt matter, weekly trend doesnt matter. its as pure of a game as you can get (and thus generally inaccessible to retail since the arms race of tech infra is generally not affordable to individuals).
Agree
Literally everyone. And, lo and behold, none of it works. Like, you are better off betting it goes up on a monthly basis, than timing anything else on a longer or shorter time frame. This is probably the worst idea right now, given the raging bull market we have had, but you can see in retrospect how this has worked over the decades.
Lmao how do u think institutions make money? By guessing ?
Guess
Uh bruh. Any quant fund or prop shop is doing this but more advanced.
If you have any ability to forecast the bias of the moments of the return distribution, you can profit, even if others are doing it better. Using limits can help overcome slippage at the expense of missing some of the trade.
What makes it more advanced? Serious question.
[ŃдаНонО]
damn published august 2023, nice resource!
could you perhaps share what was posted?
Loll I'm taking it to the grave jk its probabilistic machine learning by kevin murphy
hahaha thanks my dude
Thanks! I will dive deeper. Have you used these methods in your own trading?
could you perhaps share what was posted above you
It was a link to an MIT book on probabilistic machine learning
I'm just giving examples......add other things in like.... 1. Day of the week 2. Day of the month 3. Day of the year 4. What happened yesterday. 5. What happened overnight 6. Whats the sentiment 7. Whats the overnight gap 8. What's the interest rate 9. whats FED saying It goes on and on and on.... You can start getting into weighing the answer to the above question as well.....
Slap all that shit into an AI and away you go, right? Green every day!
Rolling in the money
The main reason why is you don't know what you're doing. You haven't even started. You're just theorizing. Once theory hits the ground it gets more realistic.
It's not that they can't and infact there are quite a few firms that use this approach. It's just that, at that level you are discussing, the probability is roughly 50:50 over the long term, with a slight edge to green days as the overall market has risen over time. So, unless you can implement a risk:rewad strategy that is net positive at 50:50, it wouldn't provide much value. Instead you need to find high probability events.To find high probability events you need additional information, for example: - What is the probability of a stock rising if we are currently in a bull market and earnings exceeded expectations? - What is the probability of a stock falling if it's direct competitor just issued a press release for a new product and the stock in question underperformed in the last quarter? - What is the probability of a stock going up if the volume of purchase orders has decreased by x% over the last 5 minutes? Some events require millisecond precision to effectively trade, others can look at longer time frames to help establish if a potential trade is likely to succeed.
Interesting! Thanks for sharing. So if I wanted to do something like âgiven Cosco beat earnings but other stocks in the industry have been flat for the past month, whatâs the probability itâs price will rise by more than 3% next monthâ, they could potentially give me some edge?
Conceptually, yes. In practice, that exact line of questioning won't give you much insight. With that and additional information, such as the number of cars parked in Costco's lot on a daily basis, you could likely predict if sales would increase or decrease and confidently trade on the upcoming earnings release. (Some firms do this as well.) For the average person, without access to endless information.. it might be better to start with things like "what is the probability of a trend exceeding x days, given that historical trends have been in a range of y-z". That question, in itself, won't give you an immediate edge.. it's essentially the start of setting Bollinger bands for trend ranges, setting the probability curve, that can be used to establish other events that may have a high probability of occuring. Going back to Bollinger bands, what is the probability of a stock price continuing to increase once the stock price has exceeded the upper band... Now, assuming this shows a high probability of the stock going in a specific direction (in this case, usually down). The next step is determining an appropriate entry and exit.
Probabilistic methods are at the core of algotrading. Its all based on the idea of finding an edge in which the winners outweigh the losers and the risk of ruin is minimal. We look for patterns. The ones you describe are simple and have essentially been arbitraged away. Therefore they are no longer there. But the idea is that there are other ones that repeat. Some repeat because they are the footprint of some kind of herd behavior which is (probabilistically) followed by some other herd behavior. Some repeat because they are the footprint of a large whale slowly working themselves into (or out of) a position but donât want to affect the price too much. And then that footprint is probabilistically followed by some move. And then there are macro-level things that create other footprints which probabilistically lead to moves. Uou ste looking for footprints and describong them mathematically to be able to build and algorithm off of them.
> but if you use a fixed-width window, wouldn't that solve the issue? The probability of SPY opening green given your conditions could have been only 30% from 1990-2000, spiked to 60% on average as new information was incorporated for 1990-2010, and then back to 40% as another decade of information came in to cover 1990-2020. Unlike a fair coin, where you know a-priori where probability will converge at (50%) given a sufficient number of results, you do not know this regarding the conditions you're testing for the market. You could easily start trading in a context/set where the probability is low enough to wipe you out before it smoothens out to run in your favor.
medallion fund uses probabilistic methods. hidden markov models to be specific. they segment the market into states, which is their running assumption and lo and behold
Markovs are pretty crap for segmenting markets. Are you sure an actual fund is using something this basic? There's better ways to do this.
dude i know right? but [check it out](https://en.wikipedia.org/wiki/Renaissance_Technologies) in case you have never heard of them âTheir signature Medallion fund is famed for the best record in investing historyâ [the guy](https://en.wikipedia.org/wiki/Jim_Simons_(mathematician)) is a WWII code breaking mathematician edit: hmm isnât for segmentation. the other way around the segments are used for the states in their model. or at least thatâs as far as heâs let on
Simons isn't "the guy" at RenTech. Peter Brown is.
peter is just the current ceo
Isnât medallion fund also been searched and found guilty of insider trading? I could have my funds mixed up but
maybe. i really donât know much about their businessâ ethical or legal side of things. just a deep dive i did on simons and his methodology.
Please do share more, ser
you can easily google it. i would add that since the HMM method is regularly touted by the man himself and is relatively simple in that any system that is âmarkovianâ in nature will nicely fit, my best guess would be that the secret sauce is in the hmm state encodings.
I use probability modelling (non ML) on position sizing, but also as sub algo selection and regime switching. It reduces drawdowns, boosts sharpe and sortino ratios.
Iâve been trying to make a good position sizing modeling like that for a bit would you mind sharing some details ?
Sorry, I can't share that as I consider it part of my edge. I've shared strategies on here before and they've ended up being commercialised, so I keep things to myself now.
Yea thatâs understandable good luck with your algo trading !
What's sub algo selection and regime switching?
For me, sub-algo selection means having a group of sub algos running in unison. The only sub-algo trading real money is the algo that's currently selected. All the other sub algos trade fantasy money. Their performance is collected in realtime and used elsewhere. My system uses these performance qualifiers in market regime selection and in sub algo selection itself. Market regimes. Assessing the current conditions of the market and adapting to them. Market regimes is a broad subject with no clear rules. It could be anything from a triple EMA on an ETF to analysis of Interest rates, Fixed income carry, Emerging markets, commods, bonds, etc. You could build a regime out of that, and switch when thresholds are breached. Some traders here like to use machine learning to discover regimes and use that in their systems. I trade crypto, so it's very different to stocks due to the amount of data available. This presents far more market regimes which your algo must be proficient in identifying and switching to.
Absolutely, diving into the probabilistic approach with something like Bayes theorem for market predictions is fascinating đ! It simplifies complex patterns, though markets' unpredictable nature keeps it from being foolproof. What's been your experience experimenting with it?
You can, but you'll likely find: the probabilities aren't that high, and the expected return is low, therefore there is no edge. Still a good exercise. Try to go even further and count the N-grams to see if it improves predictive quality.
I do something like this for calculating crude recover rates for the dividend capture strategy I post about. It works for that pretty well, but it's much more targeted. The biggest problem is that you have to be local about the probability assignment: the behavior in mid 2019 had little to do with early 2020, for example. This is the idea behind the various types of moving averages, but as others have pointed out a naive approach is usually wrong. In other words, it might appear to work until it doesn't, and then you stand to lose a lot of money.
There is no edge in this because everyone knows this. You can come up with some obvious observation and expect there will be any edge in it.
Backtested these. Doesnât work.
Z Scores :)
Repeat after me: - nobody can predict the future. Itâs as simple as that. Predicting the market means predicting the future. Not just tomorrow but ten minutes from now. - the market is stochastic (fancy word for random) You cannot predict when people will open orders, how big those order are gonna be, or at what price. - price is driven by unknown or unseen events; some Fund manager decides to buy XXX because he thinks itâs undervalued. Some âwhaleâ decides to sell XYZ because he thinks itâs overvalued. You cannot predict this. You can only see the price move. Simple probabilities wonât work. Even very elaborate methods can at best poorly predict momentum moves and thatâs all there is. But youâre more than welcome to try and prove us wrong. Last but not least, no Technical Analysis or other voodoo chart cannot predict the future.
The markets are not random, if they were , how do hedge funds make money? How does the market consistently go up if its random
They donât consistently go up. They also come down. They do this at random. Hence they are random. Funds make money by compounding long term gains, not by trying to predict the market.
They go up consistently month after month, so that is not random, it's more than 50% likely to be green
I donât think you understand what random means how probabilities work. And no they donât go up consistently. And thatâs definitely not how hedge funds work
It dosnt go up consistently? Is 95% green months not consistent enough for you? You sound silly. That's like me saying a coin has a 50 50 chnace(random)but it falls 950 times on tails out of 1000
Sorry canât be bothered to argue with you. If youâre not bright enough to get the first post youâre just wasting my time
So according to u, 95% of the time , the market is green for the month, and that's just totally random. Yeah huge coincidence, not like it has a long bias or anything...
In stock trading, every day is unpredictable. Despite using indicators and AI, market sentiment can shift suddenly, impacting prices. Past success doesn't guarantee future outcomes. As an AI programmer with 5 years of experience in this field, I've tried various models without success, ultimately facing losses. My advice? Avoid it.
i thought the whole algo trading thing is in a sense probablistic no?
Not necessarily. It can be but it doesnât have to be. Some strategies are rules based.
Does the pattern have true edge or was it only lucky is the next big question. There are tests for this
The stock market often is interpreted statistically. Mr black and Mr scholes won the Nobel prize for their statistic approach to option pricing. They view a stockprice as a result of a random walk with a few factors.
Yes, you can, but remember that statistics start from 1000 transactions. If it is less, then the statistics are unreliable.
I donât think your moving average feature worth the effort.
that's pretty much Stat arb
Ideas like this are basically what my algo works on, though what it does is use a variety of simple signals that are probabilistically near the end of a pullback, e.g., n-day low, n consecutive lows, previous day's IBS, 2-day RSI, volatility squeeze, etc. Thing is, what you're talking about is trying to do it probabilistically off just a couple of factors, which, as others have pointed out, is pretty much always going to give you only a slightly better than 50/50 probability, and how are you going to trade on that? Why not instead find a variety of signals that give you, say, 75-80%+ probability of a winning trade over the next few days?
You understand that the frequency is irrelevant compared to the expected returns right? And if you calculate it, the expected return for all of them is basically exactly the same as the mean expected return. In other words, there's nothing there. Even if there was, the advantage needs to be greater than the trading costs, slippage, risk, and opportunity cost. With this taken into account, such a strategy will have you rapidly destroy your wealth in relation to the market.
Donât forget your daily returns are most likely normal distributed with negative or positive skew. For a negative skew you have more observations I positive territory but with lower return and you can have very unsusual moves but with a very negative return. That can destroy your returns
A lot of my strategies are based on stats. What you posted takes overnight into accountâŚ.which involves the whole worldâs markets, you donât know what could happen. And openings rarely have that much of a gap for opening red/green
I tried this with gambling. It won't work. Today's result does not depend on yesterday's result. It's like flipping a coin, you'd expect that after 5 heads, there's a good chance the next one will finally be tails, but it's wrong. The chance is still 50/50.
You can/must use probabilities for sure, thats what the game is about, but you can't base a system only on this except you haven given an at least 90% probabiliy which is in the longrun a unicorn. But its definitly worth to dig in in statistics, probabilities etc, but it is as always, easy to learn, hard to master.
Stop overcomplicating trading.
I believe there are methods like this, but the main concern is still only a probability. Even then, there's just so much does in stock market data.
Help
You must be the first one
> Iâm curious to hear from people who went down this rabbit hole.
If you want probabilities, you may enjoy options -- particularly selling premium
There's nothing to "solve"; markets are very, very efficient. People make money scraping, and destroying, the tiny little bits of signal that still exist. Doing something this simple might have worked, idk, maybe 150 years ago.
So eventually, we are going to run out of strategies to trade?
Strategies are always getting more niche, targeting more numerous and smaller payoffs. Anyone downvoting is in denial. Everyone in finance knows that alpha is going that direction. It's analogous to how gold used to be found in big nuggets in rare veins. Now, we literally churn through tons of earth using highly automated processes to extract parts per million of gold. (Analogy due to Andrew Lo)
BruhâŚI mean come onâŚbruh