It's easy to lose money trading if you:
1. Trade too much (paying fees + impact on each txn)
2. Size positions too big (high vol hurts compounding ability + gets u rekt)
3. Shorting positive drift/risk premia

It's hard to lose money consistently if you avoid these things.
However clueless you are, you get to trade at market prices.

Imagine we can know that an asset has a fair value of $100.

You might think it's worth $150.

But if it's quoted $99 / $101, you can buy now at $101.

You were totally wrong but you still bought close to fair value.
The same mechanisms that make it hard to get an edge also make it hard for you to trade at really bad prices.

In a simple model, you might say that prices are set by:
- (risky) arbitrage and relative value in the short term
- pricing/valuation models in the long term
Both of these games are ultra-competitive.

It is hard to make money in the short term because competitive fast money playing "the arb game" chases relative value opportunities and "gets paid" to disperse the impact of large orders and other supply/demand imbalances.
It is hard to make money in the long term because the pricing/valuation game is so competitive that is hard to get an edge from public information that is available to everyone else
These things make it hard to LOSE money consistently too.

(As long as you're not trading too much, sizing too big, or fighting strong drift)

If you are relatively small in a liquid market, you get to trade at prices set by the best at those games.
This means that, in most liquid markets, the expected return from random trading is zero, less your trading costs.

You're equally unlikely to accidentally stumble on negative alpha as you are to stumble on positive alpha.

What are the implications of this?
Most importantly, it's critical to avoid the 3 Mortal Sins of trading too much, sizing too big, and fighting strong drift.

Those are really the only ways you can screw up with confidence.

So avoid them.
Second, type 2 errors in trading may be less harmful than type 1 errors.

Trading something with no edge doesn't hurt you *that much* (as long as it's not super hyperactive.)

It has only slightly -ve expectation (due to txn costs) but it costs you in (unrewarded) p&l volatility.
This perhaps offers an interesting asymmetrical opportunity.

If you are good at finding edges (on average), it can be a good idea to err on the side of trading stuff that looks marginal.

(And nearly everything looks marginal.)
This may be especially true for edges that make economic sense, but for which there is not enough data available to run any kind of reasonable statistical analysis.

You don't want to trade any old rubbish, but the skewed risk/reward of giving something a shot is attractive
I sometimes see people passing up (what I suspect are) good, simple edges because they're not 100% sure about it, or "the backtest doesn't look that great."

That's likely a problem of unrealistic expecatations and lack of diversification.
Any single edge is going to be noisy and uncertain.

The game is won not by finding a few perfect high-performing edges.

That's asking a bit much.

And it's an overconfident bet.

Edges come and edges go. Diversification is an operational essential, given this uncertainty.
The game is won by:
1. Avoiding the 3 Mortal Sins (trading too often, sizing too big, shorting drift)
2. Trading the most reliable return sources (Risk parity over various risk premia / MM if pro)
3. Diversifying across many different edges
And perhaps:
- worry a little less about whether a given edge is real or "good enough"
- be OK that some things just won't work out
- worry a little more about maximizing the probability that you always trading with a few good edges.

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More from @therobotjames

30 Mar
Allow me to ramble for a bit about how I think about edge in trading. 👇👇👇

First, what we're trying to do is trade deviations from fair value.

We want to repeatedly:
- buy what's cheap
- sell what's expensive
- offset risk as cheaply and efficiently as possible.

We'll concentrate on the first two here.

Let's take a really simple example to start with. Imagine you have the same asset trading on two different exchanges.

Let's pretend it's some altcoin trading on two crypto exchanges (cos I want to look cool.)

Remember we want to be trading deviations from fair value?

Well, I don't have a clue what the fair value of some altcoin should be.

But maybe I don't need to.

I can certainly identify when it might be *relatively* cheap or expensive on each exchange.

Read 16 tweets
21 Mar
Most beginner traders don't realize just how variable the p&l of even a very high-performing trading strategy is.

I simulated 10 5 year GBM processes with annual return 20% / annual vol 10%.

(Simulating a strategy within known Sharpe 2 characteristics.)
I plotted the path with the highest ending equity (green), median (black) and lowest (red).

All paths are from exactly the same process, with the same known return distribution.

You might think of the green line as trading a strategy with a known large edge and being lucky.
You might think of the red line as trading a strategy with a known large edge and being unlucky.

Even when you were really lucky, you were underwater for 130 days.

When you were unlucky, you were underwater for 508 days (about 2 years)
Read 8 tweets
21 Mar
@InBraised If you can trade for free, then your optimal trading strategy (given reasonable return estimates) would be incredibly hyperactive.

You would continuously change portfolio weights according to your latest return estimates.
@InBraised In the real world, this would kill you, because trading frictions would eat away at your PnL.

So, one way to avoid hyperactive rebalancing is to only calculate your return estimates periodically (say once every day, or every week, or something).
@InBraised But this isn't optimal because, if your alpha is good, you want to be calculating it as often as possible. You just only want to be trading when the increase in expected returns from the new position is much better than the old position.
Read 4 tweets
20 Mar
Risk and Reward: A Quant Tragedy

Through careful research, you have assembled a collection of alphas that are correlated with future asset returns.

There's some conventional stuff (momentum, ST reversal, valuation, quality, short interest etc)...

There's some totally unique stuff (you think)...

And there's some stuff you're still clinging onto because you don't want to admit you wasted all that time and money on alternative data.

You take these alphas and you combine them into an expected return for each asset.

You run some simple sense checks. Each period you sort the assets by expected return, long the top and short the bottom.

It looks good. You are encouraged.

But you wouldn't trade it like that.

Some assets are more volatile than others, many are driven by similar risks

Read 7 tweets
19 Mar
Examples of "elevator pitches" for retail-friendly trades, that I would find reasonable 👇👇👇

"Wealth management equity/bond rebalance flows are massive and, due to their size, may not be fully dispersed when performance differences (and therefore rebalance trades) are very large.

We might get paid for buying what they're selling around month-end"
"Institutional yield enhancement programs are massive and tend to be info-insensitive sellers of volatility on an up-tick in vol.

This may keep IVs depressed in the short-term, leading to trend effects in IV on significant bad news, which we could profitably trend-follow"
Read 4 tweets
18 Mar
How do I know if I have an edge?

A thread... 👇👇👇👇

I've been helping a family friend with his trading. I've given him a simple systematic strategy to trade by hand.

We can plot the distribution of historic trade returns from past trading or a backtest as a histogram.

The trade P&L is on the x-axis and the frequency (# of trades with that P&L) on the y-axis.

This is useful because it gives us a hint as to what the "edge" of our strategy might be - if we could ever truly *know* such a thing.

In this case, our strategy had positive mean and negative skew.

We saw winning trades about 58% of the time but losers were bigger, on average, than winners.

(As many things that make money tend do, regrettably)

Read 15 tweets

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