I own 4 shares in the S&P 500. I developed trading strategies to sell my shares and buy them back at key points using Fibonacci retracements. Best business ever. And if I leave it it still makes money.
I’m nowhere near $500/month but learning to trade simply has taken me years and I’m very proud of my smol accomplishment.
I tend to think this is true. I also tend to think that enough people trade based on technical trends that it should be possible to make money by predicting their behavior.
But it may not be possible for mere mortals to do this — perhaps all the profit is sucked up by HFTs with supercomputers.
A lot of people trade based on technical indicators and there's plenty of simple strategies based around moving averages that aren't complex.
In my hometown of Chicago, a lot of people used to be employed in the pits at the CBO market making. (Basically being around to take the opposite end of a trade and making small profits by selling/buying at better prices.) But those jobs were eliminated by HFTs. HFTs absolutely sucked up a ton of profits. All the traders I spoke to who had traded in the 80's, 90's and 00's said that trading got much much harder from the years 2005-2010.
Retail traders still have the ability to take speculative risk and buy/hold. It just takes a lot of time.
The difference is quantitative finance uses mathematical models where-as when 'technical analysis' is talked about in the (youtube)stock market it generally refers to opening up mspaint and using the line draw tool to 'find patterns.'
I am working on a program to backtest it. Honestly, I still have much to learn. I do things visually. So I take the little fib retracement chart tool in trading view and make the anchor at the beginning of a price movement and align the lines to the closing prices of significant support/resistance (defined mathematically as any 5 bars such that the the middle bar has the lowest low or highest high in like pyramid form.) If I see at least 3 lines of the fib retracement align, I consider the market to be demonstrating that pattern and I make a bet the market will reverse when it hits the highest fib line. This is much easier to see btw and I have a website where I’m going to write this out if you’re interested: https://seanneilan.com/ I have corresponding Python code that automates this strategy too that I intend to post once it’s ready.
No hate. But just remember that when you think that something is a good BUY, the other side (the seller) think that it is a good SELL. Hence, before trading in the stock market (or in any other market which is a zero sum game, e.g. used cars, NFT), you must be sure that you have more information than the seller.
Ofcourse, if you are not trading, or buy a company stock and hope to get dividends than there is no transaction and hence it is not zero sum. But if you are trading it is zero sum.
Of course. Part of the fun is also considering why a trade will go the wrong way too. There are infinite scenarios and part of trading is sketching why scenario X's probabililty is more likely than scenario Y's probability. And this can all be a wash when the completely unexpected happens too.
I lost about $3k doing algo crypto trading (all of that to fees - without fees I made money! aha) but learned a ton about different programming concepts and had a lot of fun.
That's awesome! :) That sounds like an amazing experience. I bet you learned a lot! And 3k is enough to make it memorable but not enough to go into massive debt or anything like that. And sure it's a loss but nobody goes into this trying to lose money. (AFAIK)
I think that trading is ultimately a creative endeavor and no matter how one trades whether it's based on phases of the moon or insider information, one needs to know when they are entering and exiting the market. Trading is an incredibly fun and creative endeavor and as long as one takes the time to precisely define what they are doing, it is great <3
Trading algorithms are often implemented in Python/Pandas so learning hackerrank problems about stacks/queues can make one better at implementing trading algorithms. Some hackerrank problems involve hashes which are verified empirically which calculus. Studying calculus can lead to better understanding of the black-scholes theorem and brownian motion.
There's even an aesthetic nature to really good trades which can lead down an interesting path on gestalt theory and composition. Drawing charts is an art form too. Check out https://www.tradingview.com/u/tntsunrise/ for examples of minimalism in the stock market.
A previous commentor on this topic wrote that I'm studying tea leaves but what if for fun I wrote an image processing algorithm that processed the position of tea leaves in a cup and traded small amounts based on that?
Even something like moving averages has applications in programming language theory. It's easier to implement a moving average in a stateful rather than functional way. It's fun to figure out faster ways of cranking out new algorithms to trade markets which leads to better productivity. Markets move really fast. Old algorithms quickly become outdated.
As long as one does not make huge risks (that aren't well thought out), one can trade based on whatever their imagination leads them to. Anything can be tried and systematically backtested. Most traders won't think outside the box but it's so much more fun when you do.
There's so much synergy between math, computer science, betting strategies, art, drawing, data science and visual theory. Everything one studies in trading can lead to 10 insights in other areas. It's just a genuinely fun topic that can lead to a lifetime of happiness.
Do you know of any studies or articles that shows why backtesting actually works or is useful? I have been reading up on automated and systematic trading, but I do not yet understand how backtesting gives one anything more than warm fuzzies.
For backtesting to work, wouldn't one need to re-run (i.e., playback and not simulate) all inputs from what the real data was (instrument pricing, the weather, social media sentiment, whatever) at a time resolution finer than what your algorithm operates on? If so, that's a massive amount of data that may even be impossible to get.
I'm not sure. I haven't found much on whether or not backtesting works in general. It definitely has its caveats. I think it really depends on what you're backtesting.
Backtesting definitely reveals ways in which trading algorithms can fail though. It can reveal more scenarios than one would initially consider which is super helpful.
Evidence-Based Technical Analysis is a starting point (book), but iirc it was able to show most common indicators aren't able to reject the null hypothesis.
I will also admit I am perhaps overconfident about my strategy. It accurately lined up to when the omicron announcement happened and then I sold my shares in the market and bought them back once I "felt" the market had finished pricing in the information. So I have half a strategy right now. My buy back strat is not great but my timing to short the S&P 500 was absolutely impeccable. And that's why I'm inspired to work on this now.
I've actively bought and sold since 2012. In that time span however, I've probably only placed like 10-20 trades over 10 years. I spend wayyy more time studying than trading. This is also a hobby and I did many other things in the meantime like studying portrait drawing, I started/sold a digital agency: https://buildthis.com/. So it wasn't actively trading. I once spent 8 months studying soybean futures after work only to wind up shorting the dow jones on a recommendation which netted me a couple grand in 30 minutes. If you think about that, that's a very small amount of money to make over 8 months. So I'm not exactly making a lot of money.
But, at the end of the day, I have not lost money. I have only made money even though it is a TINY TINY amount. I'm talking like dollars.
I’m nowhere near $500/month but learning to trade simply has taken me years and I’m very proud of my smol accomplishment.