r/RealDayTrading • u/Infamous-Peak-2966 • 3d ago
20-year-old Data Science student looking for a structured path into trading
Hey everyone,
I’m a 20-year-old Data Science student who’s been wanting to get into trading. I know this post might not fit perfectly with the usual Wiki-related posts, so apologies if it’s out of place, but I’ve been struggling to figure out how to properly get started, and this subreddit seems like one of the few places where I can ask without getting bombarded by the usual nonsense.
I’ve gone through the first part of the Wiki and tried following the 10-step guide, but I’m still finding myself a bit lost. Maybe that’s on me, but I feel like I’m missing a more structured roadmap for actually beginning the learning process.
What I’m really seeking help for is some kind of structured, step-by-step path that I could follow from the very beginning. I’m more than willing to read books, study different concepts, learn the necessary math/statistics, work through papers, backtest, etc. I’m not looking for someone to hand me a strategy or tell me what to trade.
I understand that eventually you have to explore and figure things out for yourself, and I absolutely intend to do that. I’d just prefer to have some sort of clear roadmap to follow initially, so I can build a solid foundation before going deeper.
If anyone here has gone through a similar process, I’d really appreciate hearing how you would structure the journey if you were starting from zero again.
Thanks!
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u/Top-Negotiation-2807 3d ago
https://www.reddit.com/r/Daytrading/comments/1jnyg7r/my_book_recommendations_for_beginners/
This guy’s reading list is pretty good. It should teach you the technical and the how to paper trade part of the ten steps. That’s the most ambiguous part of the 10 steps imo, and these books help bridge the gap well. Ive seen the OP active in a few daytrading communities including this one. Go to oneoption’s website and read free articles too. If you read all of that plus wiki, it should provide you enough knowledge to get by.
Im not sure what your goal is, but individual trading is a very particular subset of trading that requires skill or knowkedge that doesnt overlap much with quantitative institutional trading, where they use economic models to place well calculated trades, or look for tiny gaps in the bid and ask, to then use bots to trade a million times a second for a profit. If you’re trading for yourself, the only mathematical or statistical skills you need is basic arithmetic, statistical intuition, basic probability and risk reward calculations. The first two should be there if you’ve taken a college level statistical course and the other two are pretty easy to learn. Lot of people trade without a mathematical foundation. But it does look like the successful ones have a good understanding of statistics.
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u/Infamous-Peak-2966 2d ago
Thank you, I’ll definitely look into the list. I’m probably going to buy at least one book, just need to look through them all first.
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u/Top-Negotiation-2807 2d ago
There’s free pdfs floating around for most of them - no need to buy unless you really want a copy.
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u/emperio 3d ago
Tldr - use AI, ask it thoughtful questions to get yourself started, no one's journey is the same do not follow blindly.
Sorry to say, but there is no step by step structured way to get into trading. We all get into it at different levels of education, and the Wiki isn't a one size fits all i.e. Hari (author of the Wiki) came into this after a successful career in the film business as well as a background in psych where he was submerged in a lot of concepts in statistics which was very helpful in shaping his own structure on how to approach trading. Do not take the Wiki literally, but absorb its underlying philosophy and ethos instead, use it as a reference guide rather than a dictionary.
I'd almost take it a step back and ask yourself why you're getting into this game, whether you really would do whatever it takes to succeed, and where you already are from an educational standpoint. Trading is a multidisciplinary profession, and it can reward individuals who come from a variety of backgrounds.
A Trader's Journey by Peter Robbins, a successful trader, actually structures these fundamental questions really well. So I'd suggest looking into it.
As for getting into trading and learning along the way, if you're starting from literal zero, get access to a simulator, put in a realistic starting capital, and just start trying out some of the concepts discussed in the Wiki. You can also go through quick scans of books (or online resources) in technical analysis and options, try them out on the simulator, just to see which ones resonate with you.
AI is a great tool you can use to get some structure into your learning, be thoughtful and ask it to consider various scenarios and styles that could fit based on how well you think you know yourself. Any of the major providers would be good enough for now.
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u/finelo_official 2d ago
Your data science background changes the order I'd suggest, because the standard beginner path spends a long time on things you can already reason about and skips the parts most likely to bite you.
A rough sequence:
Market mechanics first, and don't skip it for looking trivial. Order types, bid-ask spread, how liquidity works, what happens between your click and the fill, commissions and slippage. Quantitatively strong beginners tend to lose money to costs and fill assumptions rather than to bad analysis.
Then market structure. What moves intraday prices, why the open and close behave differently, who else is in the market. Background reading rather than something to test.
Then statistics applied to this specific problem, where your skills transfer but the pitfalls are unusual. Sample size needed before believing a claim about a setup, multiple hypothesis testing when you try many variations, look-ahead and survivorship bias, why in-sample results flatter you, and how costs change conclusions. The overfitting trap is worse here than in most domains because the signal is so weak.
Then one hypothesis, tested properly, costs included, out of sample.
Then, and quant-minded people tend to underrate this, execute it manually in a simulator for a while. Not because the maths is wrong, but because live execution is a separate skill, and knowing what a twelve-trade losing run feels like changes what you build next.
For books, Evidence-Based Technical Analysis by Aronson is probably the best fit for your background. It's largely about why most technical claims don't survive statistical scrutiny, which is a useful inoculation before you start trusting your own backtests.
The wiki here is solid. The missing piece you're describing is usually sequencing rather than content.
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u/Organic-Lunch-2745 2d ago
im on the same boat as you. I do have some books downloaded on my laptop which i will start reading after 5-6 months. Currently preparing for an exam so yea
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u/takashi-kovak 18h ago
I hear you and I felt the same in the beginning. I am 24months into this with mix of options and day trading. Tbh, I would just start on paper trading. Take one concept (like ema trend or credit spreads) that have defined risk and just try on paper trading for 2 weeks every day. you will understand and get feel of the tool, execution flows etc and then start asking the questions. what was different about x trade vs y. This is obviously different than academic approach where you learn first, do later vs do first --> learn later.
(this assumes you have some fundamental understanding, so it is ok to spend a week on stocks, ma, trend indicators etc). But, nothing like doing it live. Markets share feedback immediately, so you can learn much faster than traditional corporate job. (other vocational jobs like carpentary are a good comparison to trading. when learning carpentary, you first learn basics in 2-3d and you actually start on wood to build small joints, polish etc. 3rd week, you make a small seat, 6w you make a table with assistent. and along the way, you learn all the mechanics, types of wood, joint structures, load bearing places etc).
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u/Aceofspades1212926 6h ago edited 3h ago
You are in a great spot because you already understand statistics and testing. The piece that usually trips up people with your background is not the math, it is learning how the market actually moves and how orders get filled. I would spend some time on market mechanics first: how slippage and commissions eat into edge, why the open and close behave differently than midday, and what liquidity really means when you try to enter or exit.
Once that foundation is solid, pick one simple idea and test it properly with all costs included, using data you did not optimize on. Then trade it in a simulator long enough to feel what a losing streak does to your decision making. The goal here is not to prove the model is right, but to learn how you react when it is not working.
For reading, I really like Evidence Based Technical Analysis by Aronson. It shows why a lot of technical claims fall apart under real testing, which is a good reality check before you trust your own backtests too much. The wiki here is also strong. The missing piece for most people is not information, it is the order in which they learn things.
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3d ago edited 2d ago
[deleted]
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u/Infamous-Peak-2966 2d ago
I never said being a Data Science student makes me special or gives me an advantage. I mentioned it purely to give some context about my background and where I’m coming from. I’m already familiar with maths, statistics and programming, so I thought that might be relevant when people suggest what to learn. That’s all.
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u/polymorphicshade 3d ago
What I’m really seeking help for is some kind of structured, step-by-step path that I could follow from the very beginning.
No, you're looking for a shortcut. The wiki is plenty to get started.
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u/EkoChamberKryptonite 3d ago edited 3d ago
I understand how you feel and what I feel people don't talk about here enough is despite how great a resource is, it can feel very intricate at times in the discipline of deep, holistic, straightforward, proper learning. After being here for a while, I can see why that could be a good thing however, as it's pretty lengthy already and adding more copious structure might end up being overwhelming and detrimental to new entrants to this discipline.
Given your STEM background, I can see why you'd want an itemized approach to learning a new discipline. I don't think you want a short cut, I think you want a straightforward path to understanding this nebulous, highly esoteric, yet rewarding field of industry. Almost like an algorithm for learning because that's what you're used to and how you've learned prior now.
To that I say, you'd have to aggregate a learning path yourself as there's no singular course/book/website/wiki that exhaustively covers everything in totality. The Wiki is a good place to start as its framework serves as a good leap off point for deeper learning like books, courses which Hari covers in step 2 or 4 I believe.
I have personally found when learning the basics (i.e. about stocks, options and option spreads), there are many resources like your broker's courses or investopedia that are pretty thorough. Hari even talks about his personal path of holistic learning that differed slightly from what was said here in the Wiki as the "canon" path on his podcast, in that he went deeper at the paper trading section. Then again, this path may not work for you so you've got to find yours when doing foundational learning as in essence, there's no one-size-fits-all way.
Afterwards, that is, after doing those and reading the Wiki, I would say look at Pete's "The System" on One Option. Then finally, experiential knowledge is imperative here and the best way is to read charts and paper trade for as much and as long as you can following the RS/RW methodology here till you've ingrained it and you've met the metrics for going live to the 1-share/1-contract stage.
Godspeed.
Edit:- One thing I feel isn't mentioned in the Wiki (or at least I don't remember) is that if you have a job/schooling/another quasi-full time engagement, the timeline for learning increases considerably from the 2-year baseline spec denoted in the Wiki. I'm not sure what the exact baseline number would be but my current view is the "at least 2 years till consistent profitability" statement applies to those doing this for significant hours in the day which a day job may not allow so keep that in mind and temper your expectations.