You Suck At Day Trading

Your Backtest Doesn't Know You Exist

2026-05-21T16:45:20Z

You ran 10,000 simulations and they all made money. Congratulations. None of them were real.

The most dangerous backtest is the one that confirms what you already believe.

The data generation problem

Every backtest starts with a dataset. And every dataset is a survivor.

The historical data your strategy runs on has representation issues baked into it. Delistings, halts, and blown-up tickers are either missing or poorly represented. The rare events that would destroy your strategy — flash crashes, liquidity vacuums, circuit breakers — are, by definition, rare in the sample.

You cannot stress-test for black swans using a dataset that barely contains them.

This doesn't mean backtesting is useless. It means the confidence interval is wider than most traders want to admit. Your backtest tells you what would have happened in a world that already happened, with data that already survived.

There is no "fair market value"

Price is a construct. The number on your screen is the last trade — not some objective measurement of what an instrument is worth.

Many traders operate on the assumption that the exchange price IS the market price. It isn't. Not exactly.

Consider futures: the composite price you see is often built from multiple venues and contract months using merge strategies. How that price gets assembled — first-traded, last-traded, volume-weighted, bid-ask midpoint — changes what the number means. Most traders never look at how their data vendor builds the tape. They just trust the line on the chart.

That line is an editorial decision, not a fact.

If your strategy depends on precise entries and exits, you're building on a price that was constructed by someone else's methodology. And if you don't know what that methodology is, you're trading someone else's assumptions as if they were your own.

Market impact is unmodeled

Here is the part that almost no simulation captures: your orders move the market.

If your strategy hits 100,000 contracts on the bid, the bid moves. The fill you modeled at the theoretical price doesn't exist anymore. The liquidity you assumed was there evaporates the moment you try to take it.

Backtesting doesn't simulate this. Paper trading doesn't simulate this. Market replay doesn't simulate this either.

Replay features are impressive engineering — they reconstruct the order book, they play back time and sales, they let you practice in "real" conditions. But the book you're seeing is the book that existed without you in it. The moment you participate at scale, you change the thing you're measuring.

This isn't a software limitation. It's a physics problem. You cannot observe a market and participate in it simultaneously without altering the observation.

The three false comforts

Backtesting, paper trading, and market replay share the same structural flaw: they simulate a market that doesn't know you're in it.

Backtesting runs your logic against historical data and assumes perfect fills at historical prices. It doesn't model slippage, partial fills, or the market's reaction to your orders.

Paper trading lets you practice execution in real time without capital. But the fills are synthetic — you get filled at prices that the real market may not have offered you, because your order didn't actually compete for liquidity.

Market replay reconstructs historical sessions and lets you trade them as if they were live. It's the closest approximation — and it still can't model how the market would have responded to your participation.

All three are useful for learning mechanics, testing logic, and building discipline. None of them tell you what happens when real money enters a real book.

What honest testing actually requires

If the tools are flawed, what do you do?

You use them anyway — but you stop treating their output as proof. A backtest is a hypothesis, not a conclusion. Paper trading is rehearsal, not performance.

Honest testing means:

acknowledging that your dataset has survivorship bias and limited tail events,
understanding how your data vendor constructs price,
sizing your live positions as if the backtest overstated your edge (because it probably did),
tracking live performance separately from simulated performance and comparing honestly,
and accepting that the gap between simulation and reality is not a bug — it's the cost of doing business.

The traders who last aren't the ones with the best backtests. They're the ones who understood what the backtest couldn't tell them.

Open questions

What was your biggest gap between backtest results and live performance?
Have you built a system that actually survived contact with real markets? What did you have to change?
How do you account for data quality when evaluating a strategy?

You Were There: The Flash Crash

2026-05-21T16:45:20Z

May 6, 2010 — The day the plumbing exploded

Complex systems don't fail gracefully. They fail by suddenly revealing what they always were.

2:32 PM ET

The tape screams.

E-mini S&P 500 futures contracts begin an unprecedented descent into the void. Liquidity hasn't just dried up — it's been deleted. You're staring at an order book where the bid-ask spread has widened to a canyon.

The Big Plug has been pulled.

Your terminal displays a waterfall of red as high-frequency algorithms enter a feedback loop of systemic self-destruction.

Welcome to the Flash Crash.

What I Saw

I was just getting back from my lunch break, wondering if I was connected to a broken paper feed.

This was maybe my fourth day on the job at a trading technology company. I'd just come from working at tradingcomputers.com, where I'd spent months hearing customers kick their machines as they smoked through their 7200 RPM drives. Our Raptor customers probably had better luck, but still — I'd seen what market chaos did to people and their equipment.

But that was a slow-motion disaster spread across months. This was different.

There was a point where we all sort of collectively stood up and looked around the room.

"Uhh... is this gonna stop?"

Nobody answered. Because nobody knew.

It was hysteria for the next few minutes. Phones ringing. Screens refreshing. People just staring at numbers that didn't make sense.

And then — it was over.

Like a fire drill we weren't prepared for but had all rehearsed.

What Actually Happened

At 2:32 PM, a mutual fund company executed an algorithm to sell $4.1 billion worth of E-mini S&P 500 futures contracts. The algorithm was set to execute based on volume, not price or time.

That's the technical explanation. Here's what it meant:

The algorithm didn't care what price it got. It just kept selling. And selling. And selling.

High-frequency trading firms — the ones who normally provide liquidity by sitting on both sides of the order book — saw the waterfall and did the rational thing: they stepped back. Why catch a falling knife?

With no one willing to buy, prices went into freefall.

The Dow Jones Industrial Average dropped nearly 1,000 points in minutes. Procter & Gamble, a blue-chip stock that your grandmother probably owns, traded at a penny. Accenture went to zero.

Zero.

Then, just as suddenly, it snapped back. By 3:07 PM, most of the losses had been erased.

Total elapsed time: about 36 minutes.

What It Revealed

That day taught me something I've carried ever since:

Market structure is plumbing. And most people never think about plumbing until it explodes.

The Flash Crash revealed:

Liquidity is a lie. The order book looks deep until everyone runs for the exit at once. Then you discover how thin it really was.
Algorithms don't panic — but they don't think either. They execute instructions. When those instructions meet edge cases nobody anticipated, the results are catastrophic.
Speed is a weapon. The HFT firms saw the crash coming microseconds before everyone else. They got out. Retail traders staring at delayed feeds? They got slaughtered.
Circuit breakers didn't exist yet. The market had no pause button. It just kept going until the tape ran out of prices to print.

The Aftermath

The SEC eventually implemented circuit breakers — automatic trading halts when prices move too fast. They also created the "Limit Up-Limit Down" rule to prevent stocks from trading outside certain bands.

But here's the thing: those rules exist because someone had to watch P&G trade at a penny first. Someone had to watch Accenture go to zero. Someone had to lose real money before the adults in the room decided maybe the system needed guardrails.

That's always how it works.

Why This Matters to You

If you're day trading from your home office in 2026, you're competing against systems that can:

See order flow before you do
Execute trades in microseconds
Pull liquidity the moment things get weird

You're the guy on the 2400-baud modem in 1989. You're the guy staring at a delayed feed in 2010. You're the guy who finds out what the price was after everyone else has already traded on what it is.

The Flash Crash lasted 36 minutes. In that window, fortunes were made and lost. And the people who made money? They weren't sitting at home wondering if the feed was broken.

They were the ones who broke it.

This is part of the "You Were There" series — first-hand and reconstructed accounts of market events that broke people, systems, and assumptions.

You Were There: Friday the 13th Mini-Crash

2026-05-21T16:45:20Z

October 13, 1989 — The day the LBO dream died

The silence of your home office suddenly becomes a liability. On a trading floor, you'd hear the roar of the crowd first. Here, you just see a ticker freeze.

9:30 AM ET

The morning starts deceptively well.

You're sitting in your home office — a rare privilege in 1989 — staring at the amber glow of your new setup. It's likely a dedicated dumb terminal, or perhaps a high-end 386 PC running DOS with a modem that screams a 2400-baud handshake every time you connect to the exchange.

The coffee is fresh. The house is quiet. The novelty of avoiding the commute feels like a victory.

The market opens flat. Sleepy, even. For a few hours, you think your boss was right: it's just a lazy Friday.

2:40 PM ET

The "lite trading day" evaporates.

1. The Catalyst: UAL News

The silence of your home office suddenly becomes a liability.

On a trading floor, you'd hear the roar of the crowd first. Here, you just see a ticker symbol freeze — or a headline crawl across the bottom of your screen in pixelated monochrome text:

UAL BUYOUT COLLAPSES.

The deal for United Airlines — the one everyone was banking on to keep the leveraged buyout boom alive — has failed. Financing couldn't be secured.

In an instant, the entire thesis of the 1980s bull market is in question.

2. The Lag

This is the most terrifying part of trading from home in '89.

As panic selling hits, the data feed to your remote terminal starts to choke. The quotes aren't real-time anymore — they're delayed by seconds, then minutes.

You see prices ticking down, but you know the reality on the floor is already far worse.

You try to punch in a sell order. The command line hangs.

The blinking cursor mocks you.

3. The Freefall

The Dow Jones Industrial Average doesn't just dip. It dives.

In a matter of minutes, the index sheds over 190 points. In 2026 numbers, that sounds like nothing. But in 1989, that's nearly 7% of the entire market vanishing in an hour.

It's the second-largest point drop in history at that time — dwarfed only by the '87 crash two years prior.

Junk bonds, which fueled the entire decade's excess, are suddenly toxic. You watch symbols for arbitrage plays turn into falling knives.

4. The Isolation

You desperately call the desk.

Busy.

You call again.

Busy.

Without the shouting of colleagues or the squawk box to guide you, you're flying blind in a storm. You realize why "work from home" hasn't caught on yet — when liquidity dries up, being the guy on the phone instead of the guy in the pit is a dangerous disadvantage.

The information asymmetry isn't theoretical anymore. It's the difference between getting out and getting buried.

5. The Close

The market closes, mercifully, stopping the bleeding.

The Dow finishes down 190.58 points.

You sit back in your chair, the hum of the computer fan the only sound in the room. You've just witnessed the end of the roaring '80s LBO era, all from your spare bedroom.

You suspect that come Monday morning, your boss might not be so keen on this "telecommuting" experiment anymore.

What It Revealed

The Friday the 13th Mini-Crash wasn't as catastrophic as Black Monday two years earlier. But in some ways, it was more instructive:

Leverage cuts both ways. The LBO boom was built on borrowed money. When one deal collapsed, it revealed how fragile the entire structure was.
Remote trading is a disadvantage. The lag, the busy signals, the isolation — these weren't just inconveniences. They were the difference between executing a trade and watching helplessly as your position disintegrated.
Information travels at different speeds. The guys on the floor knew before the tape did. The guys at home found out last.
Market sentiment can turn in an instant. One failed deal. That's all it took to end a decade of irrational exuberance.

The Aftermath

The crash accelerated the end of the junk bond era. Michael Milken, the "Junk Bond King," would be indicted just months later. Drexel Burnham Lambert, the investment bank that had fueled the LBO boom, would collapse in 1990.

The markets recovered, eventually. They always do.

But the people who got caught on the wrong side of a 2400-baud modem that Friday afternoon? They learned a lesson that wouldn't be taught in any trading course:

When the market moves, it doesn't wait for your connection to catch up.

Why This Matters to You

You might think 1989 is ancient history. Modems are gone. Terminals are gone. We have fiber optic connections and sub-millisecond execution now.

But the fundamental problem hasn't changed.

When volatility spikes, retail platforms lag. Order routing slows down. The "instant" execution you were promised turns into a queue. And while you're waiting for your sell order to fill, someone with better infrastructure has already gotten out.

You're still the guy in the spare bedroom.

The pit just moved to a data center in New Jersey.

This is part of the "You Were There" series — first-hand and reconstructed accounts of market events that broke people, systems, and assumptions.

What Insiders File (And Retail Ignores)

2026-05-21T16:45:20Z

The Flash Crash made one thing clear: you cannot compete on speed.

The speed problem is solved (against you)

If you read the Flash Crash piece, you already know the punchline. The firms that made money on May 6, 2010 weren't sitting at home refreshing a screen. They had co-located servers and direct market access. They saw the order book collapsing microseconds before anyone else and got out.

That advantage hasn't gone away. It's gotten wider.

Algorithmic trading broadly accounts for somewhere between 60 and 75 percent of U.S. equity volume, depending on who's counting and what they include. High-frequency trading is a subset of that, but even the subset operates at speeds and budgets that retail can't touch. You are not going to out-speed them from your home office.

So if you can't compete on speed, what can you compete on?

One category of data that doesn't care about speed

When a CEO buys shares of their own company with their own money, they have to tell the SEC. It's called a Form 4 filing.

Corporate insiders (officers, directors, anyone who owns more than 10% of a class of a company's equity securities) are required to report their transactions to the SEC within two business days. These filings are public and free. They're posted to EDGAR, the SEC's electronic filing system, where anyone can read them.

No license required. No data vendor subscription.

A CEO buying $2 million of their own stock on the open market is a different kind of signal than an algorithm executing a statistical arbitrage strategy in 40 microseconds. One tells you something about conviction. The other tells you something about speed.

You can't compete on speed. Conviction doesn't have a latency requirement.

Why this data matters

When I started looking into this, I expected the academic research to be thin. It isn't. A Wharton study covering 22 years of data found that insider purchase portfolios outperformed the market by about 6% annually. Other studies put the number higher. The finding keeps showing up. Not always. Not reliably enough to be a trading system by itself. But consistently enough that I couldn't ignore it.

The reason isn't complicated. When someone who sees the internal numbers, the pipeline, and the board presentations decides to write a personal check for company stock, they know something the market doesn't. Sometimes they're wrong. But they're wrong less often than random.

The catch is that most insider transactions are noise. Stock grants, option exercises, automatic sales under 10b5-1 plans — those happen on schedule regardless of outlook. I had to learn to filter all of that out.

Open-market purchases are the signal. Nobody is forced to buy. When a CFO puts $500,000 of their own money into shares, that's a voluntary bet.

Why retail traders ignore it

Form 4 data is boring.

It's not real-time. It doesn't fit into the dopamine loop of scalping or the narrative loop of technical analysis. Nobody is selling a course on "How to Read SEC Filings."

The filings themselves are XML documents. Structured, but not pretty. Parsing them requires knowing what fields matter, filtering out the noise transactions, and understanding context. Is this a cluster buy? Is the insider buying for the first time? How does the purchase size compare to their existing holdings?

Most retail traders don't even know EDGAR exists. The ones who do usually don't have the patience or tooling to monitor it systematically.

How I ended up here

I spent years trying to build trading tools. Backtesting engines, signal generators, portfolio optimizers. Every approach hit the same wall: data costs.

Licensed market data kills your economics before you write a line of code. A basic historical equities dataset starts at hundreds per month. Real-time feeds are thousands. And the kind of data you'd need to build and distribute a useful product requires licensing agreements that a solo developer can't afford.

I kept circling back to the same question: what data is free, public, and actually worth something?

Form 4 filings kept being the answer. Public, free, filed within two business days, structured XML with consistent fields. No redistribution restrictions. No licensing problems.

The economics work because the SEC already made the data public. I'm not reselling someone else's feed. I'm reading what's already there.

What I built (and what I didn't)

I built a pipeline that watches for Form 4 filings and surfaces the ones that look interesting. Open-market purchases by officers and directors, filtered from the noise of grants, exercises, and scheduled sales.

I should be transparent about what this is and isn't.

I don't trade. I don't have the capital to survive drawdowns, and I know enough about myself to know I'd be bad at it. I don't sell signals. I don't give investment advice. I don't look at the insider data and make personal trading decisions.

What I do is build plumbing. The Flash Crash taught me that market structure is plumbing, and plumbing is what I understand. So I built a system that monitors a specific, public, under-watched data source and makes it easier to pay attention to.

Open questions

Have you ever used insider filing data in your own analysis? What did you find?
What public data sources have you found that are genuinely useful and free?
If you've tried to build trading tools, where did you hit the data cost wall?

If you want this data without watching EDGAR yourself

I built Matchstick Radar for exactly this.

It tracks SEC Form 4 filings and makes the feed easier to inspect. No trading signals. No predictions. If you want to kick the tires, the quickstart gives you 25 free Discord credits to try /form4.

If it becomes useful enough to keep, paid plans are Starter at $49/mo and Pro at $79/mo.

The Rules Don't Apply to Me

2026-05-21T16:45:20Z

A profile of the most expensive belief in retail trading.

Confidence is an asset. Confidence that the rules do not apply to you is a liability with no hedge.

The Setup

You've been at this for two years. Maybe three.

You blew up once, rebuilt, and now run something that mostly works. You stopped doing beginner mistakes.

You feel different from the people who usually get warned.

That feeling is the tell.

The Argument

It sounds like this:

The recordkeeping rules and adviser requirements are for banks, hedge funds, and institutions. Not for someone running $200k from a home office. Not for someone selling a $99/month Discord with trade alerts. Not for someone managing a small account for a friend.

The logic feels reasonable. It is still wrong.

Where It Breaks Down

Regulations are usually tied to activity, not self-image.

The Investment Advisers Act does not include a carve-out for people who think they are too small. Futures and securities oversight does not disappear because operations are informal.

The rules exist because markets repeatedly produce the same pattern: people scale behavior faster than they scale accountability.

The Belief That Gets People

It is rarely "I am above the law."

It is usually: "No one is coming after someone like me."

That may feel true for a while. But "unlikely to be caught soon" is not the same as "compliant." When you cross certain thresholds such as AUM, client count, visibility, or complaints, everything can change quickly.

The people most surprised are often the ones who believed they were safe because they were small.

What the Industry Sells You

Trading media has an incentive to keep this blurry.

If people understood the exposure tied to selling signals or managing outside capital, they would ask harder questions about the people teaching them.

Forums reinforce confidence. Discords reinforce confidence. Screenshot culture reinforces confidence.

None of that changes legal obligations.

The Honest Version

The operators who build durable businesses usually take rules seriously before they have to.

Not because they are naturally conservative, but because they understand that compliance and recordkeeping are part of risk management.

Ignoring this does not make the burden go away. It just delays when it shows up.

Open questions

What compliance requirement caught you off guard when you started taking trading seriously?
Have you seen a community or signal service shut down for regulatory reasons?
At what point did you realize "hobby" had become "business" in the eyes of a regulator?

The Pipeline Nobody Warns You About

2026-05-21T16:45:20Z

You didn't plan to manage other people's money. It just kind of happened.

The most dangerous moment in a retail trader's career isn't when they blow up. It's when they don't.

It Starts With a Good Year

You had a system. You tested it. You ran it live and it worked.

Not life-changing money, but real money. Consistent enough that you started keeping a spreadsheet. Consistent enough that people noticed.

Your brother-in-law asks how you're doing it. You explain the setup. He asks if you can do it for him too.

You say no at first.

Then you say: maybe.

The Offer That Seems Fine

It's informal. It's family. It's a small amount, maybe $10,000 or $25,000. Nothing that feels like it needs lawyers or paperwork.

You're still trading your own account the same way. This is just the same thing with a little extra capital.

What could the problem be?

What You Just Did

You may have become an investment adviser in the legal sense.

Under the Investment Advisers Act of 1940, if you are in the business of advising others on securities for compensation (direct or indirect), you can fall under adviser rules. Managing someone else's money and keeping a cut of profits can count. Trading under discretionary control can count.

The threshold is often about activity, not self-description.

The Pipeline

Here's what most trading education content skips:

Retail trading has two exits for people who do well enough to get attention.

Exit one: prop trading. You get funded by a firm and now operate under their compliance requirements, risk limits, and recordkeeping obligations.

Exit two: selling the system. Courses, signals, newsletters, Discord alerts. If you are publishing actionable ideas to paying subscribers, you are taking regulatory risk whether or not you call it "education."

Most traders who improve eventually face one of these branches. The risk comes from pretending they don't.

Why This Keeps Happening

The industry is built to funnel people here without naming it.

Courses teach entry tactics. Communities reward PnL screenshots. Social platforms reward broadcasting confidence. The next step, charging for access or taking outside capital, feels like graduation.

It is graduation into a regulated environment many people never prepared for.

The Question Worth Asking Now

If someone asked you to reconstruct every decision you made in the last 90 days, with inputs and timestamps, could you do it?

If the answer is no, that is a structural weakness, not just an admin problem.

Open questions

Have you been approached to manage someone else's money? What happened next?
Did you ever cross into selling signals or running a Discord without realizing the regulatory implications?
What's the smallest amount of outside capital that made you realize you were in over your head?

And you're ignoring your failures.

2026-05-21T16:45:20Z

How many stocks that went to zero are in your backtest?

None?

That's worth sitting with. If your dataset only contains survivors, your conclusions are built on a lie of omission.

The two flavors of self-sabotage

Most self-sabotage in trading looks like one of two things: fear or ego. Fear makes you cut winners early. Ego makes you hold losers forever.

Both feel like discipline in the moment. Neither is.

Every trade has a cost

Even the "free" ones.

The zero-sum reality

In the short term, trading behaves like a zero-sum competition before fees, and a negative-sum game after costs. Someone is taking the other side, and they are often better equipped than you are.

Retail traders usually lose on:

execution quality,
information speed,
transaction costs,
and emotional discipline.

Your capital and infrastructure are not built to beat firms that do this for a living. That's not a judgment call — it's a resource mismatch.

Survivorship bias doesn't care about your objectivity

Even if you think you're being rational, survivorship bias still sneaks in. You can't fully escape it; you can only design around it. More on that here.

You're still ignoring your failures

You keep underpricing risk because the upside fantasy feels better than boring accuracy.

Cost

Start with the obvious: "free" trading is not free.

If you aren't paying explicit commissions, you're still paying through spreads, slippage, payment-for-order-flow dynamics, financing costs, or product upsells. The bill always shows up somewhere.

And then there are the secondary leaks: data subscriptions, premium tools, and recurring fees that look small in isolation and brutal in aggregate.

After-tax returns

Are you being honest with yourself here? What about to the IRS?

Run the math after taxes, not before.

Short-term gains are taxed harder. Frequent turnover increases tax drag. If your edge is thin, taxes can erase it entirely.

Volatility vs over-under performance

Volatility distorts your self-evaluation.

In drawdowns, you feel uniquely cursed. In bull runs, you feel uniquely skilled. Both are usually false.

Performance is contextual. Measure against a benchmark and full-cycle results, not whatever mood the tape gives you today.

And you're failing the test

2026-05-21T16:45:20Z

Most traders fail the marshmallow test every single day. They want the reward now. They confuse activity with progress. And they keep grabbing.

The marshmallow test is a test of structure

The original test is simple: one marshmallow now, or two later.

In markets, the same trade-off plays out constantly:

impulsive trade now,
or disciplined position sizing and patience for better odds later.

The people who delayed gratification in the classic studies generally did better long term. Not because they had superhuman willpower, but because they built systems to manage impulse.

That's the point for trading too: willpower fades. Structure doesn't.

Impulse always wins until you stop trusting it

Promise a bigger reward and most people still grab the first one. That's what revenge trading, overtrading, and panic exits are — the marshmallow wins every time.

Guardrails for when price goes feral:

step away from the screen,
predefine entries/exits,
reduce position size,
and reread your own rules before you click.

Patience doesn't guarantee profits. But impatience reliably compounds mistakes.

And the only limit order is yourself

2026-05-21T16:45:20Z

Most people who try day trading lose money. The reasons are predictable, structural, and largely self-inflicted.

The logical errors that do the most damage

Everyone talks about sunk cost bias: you keep feeding a position because you've already fed it too much. You know the trade is dead, but you keep paying tuition.

That's not the only bias working against you.

Many traders convince themselves they can predict short-term moves with enough indicators, enough screens, or enough "edge." They cannot.

Another classic: you remember your winners like movie highlights and memory-hole your losers. That gives you fake confidence and terrible sizing.

And some people eventually trade compulsively. At that point, logic leaves the room and position size becomes mood regulation.

Addressing the unconscious biases

Your brain is built for speed, not market truth. It uses shortcuts from past experiences, beliefs, and ego protection. Useful in daily life. Dangerous in markets.

Sunk cost bias: You keep investing because you've already invested, not because the thesis still works.
Survivorship bias: You study visible winners and ignore the graveyard.
Selection bias: You compare yourself to cherry-picked outliers and call it a plan.
Compulsive trading: You chase emotional relief, not expected value.

Modern portfolio theory (MPT)

Modern Portfolio Theory is not sexy, and that's exactly why it works. The core idea is simple: portfolio construction matters more than hero trades.

It forces you to think in probabilities, diversification, and risk-adjusted returns instead of vibes.

What MPT gives you:

A framework for balancing risk and return.
A discipline for position sizing and diversification.
A way to evaluate results without lying to yourself.
A defense against over-concentration and narrative addiction.

Key Indicators of MPT

The efficient frontier is the set of portfolios with the highest expected return for a given risk level.
The capital asset pricing model (CAPM) is a model that describes the relationship between expected return and risk for a security or portfolio.
The Sharpe ratio tells you how much return you got per unit of volatility.

You can keep pretending you're one indicator away from mastery, or you can build a system that survives your own biases.

You Suck At Day Trading

Your Backtest Doesn't Know You Exist

The data generation problem #

There is no "fair market value" #

Market impact is unmodeled #

The three false comforts #

What honest testing actually requires #

Open questions #

You Were There: The Flash Crash

2:32 PM ET #

What I Saw #

What Actually Happened #

What It Revealed #

The Aftermath #

Why This Matters to You #

You Were There: Friday the 13th Mini-Crash

9:30 AM ET #

2:40 PM ET #

1. The Catalyst: UAL News #

2. The Lag #

3. The Freefall #

4. The Isolation #

5. The Close #

What It Revealed #

The Aftermath #

Why This Matters to You #

What Insiders File (And Retail Ignores)

The speed problem is solved (against you) #

One category of data that doesn't care about speed #

Why this data matters #

Why retail traders ignore it #

How I ended up here #

What I built (and what I didn't) #

Open questions #

If you want this data without watching EDGAR yourself #

The Rules Don't Apply to Me

The Setup #

The Argument #

Where It Breaks Down #

The Belief That Gets People #

What the Industry Sells You #

The Honest Version #

Open questions #

The Pipeline Nobody Warns You About

It Starts With a Good Year #

The Offer That Seems Fine #

What You Just Did #

The Pipeline #

Why This Keeps Happening #

The Question Worth Asking Now #

Open questions #

And you're ignoring your failures.

The two flavors of self-sabotage #

Every trade has a cost #

The zero-sum reality #

Survivorship bias doesn't care about your objectivity #

You're still ignoring your failures #

Cost #

After-tax returns #

Volatility vs over-under performance #

And you're failing the test

The marshmallow test is a test of structure #

Impulse always wins until you stop trusting it #

And the only limit order is yourself

The logical errors that do the most damage #

Addressing the unconscious biases #

Modern portfolio theory (MPT) #

Key Indicators of MPT #

The data generation problem

There is no "fair market value"

Market impact is unmodeled

The three false comforts

What honest testing actually requires

Open questions

2:32 PM ET

What I Saw

What Actually Happened

What It Revealed

The Aftermath

Why This Matters to You

9:30 AM ET

2:40 PM ET

1. The Catalyst: UAL News

2. The Lag

3. The Freefall

4. The Isolation

5. The Close

What It Revealed

The Aftermath

Why This Matters to You

The speed problem is solved (against you)

One category of data that doesn't care about speed

Why this data matters

Why retail traders ignore it

How I ended up here

What I built (and what I didn't)

Open questions

If you want this data without watching EDGAR yourself

The Setup

The Argument

Where It Breaks Down

The Belief That Gets People

What the Industry Sells You

The Honest Version

Open questions

It Starts With a Good Year

The Offer That Seems Fine

What You Just Did

The Pipeline

Why This Keeps Happening

The Question Worth Asking Now

Open questions

The two flavors of self-sabotage

Every trade has a cost

The zero-sum reality

Survivorship bias doesn't care about your objectivity

You're still ignoring your failures

Cost

After-tax returns

Volatility vs over-under performance

The marshmallow test is a test of structure

Impulse always wins until you stop trusting it

The logical errors that do the most damage

Addressing the unconscious biases

Modern portfolio theory (MPT)

Key Indicators of MPT