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)
You have a strategy with a *known* Sharpe 2 edge, and you might reasonably be underwater for at least 2 years if your luck falls in the bottom decile.

The reality of trading is even more uncertain because you never *know* what your edge is, see:
This is why we diversify across return sources/strategies/alphas.

It's not just for the well-understood vol-reducing diversification benefits.

It's also because, by spreading our bets, *we decrease, the chance that we are trading without an edge*.
If you are serious about this game, you need to understand this dynamic deeply.

And structure your life, income, expenses so this level of trading p&l variance is appropriate.

The "carry" of a salary, consulting work, or side gig is often helpful here.
And, of course, management fees.

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More from @therobotjames

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)...

1/7
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.

2/7
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

3/7
Read 7 tweets
19 Mar
Examples of "elevator pitches" for retail-friendly trades, that I would find reasonable 👇👇👇

1/4
"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.

1/n
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.

2/n
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)

3/n
Read 15 tweets
9 Mar
On "stationarity"...

When we talk about something being "stationary" we mean that the observations look like they could be drawn from the same "bag of observations" (distribution), regardless of what time we choose to look at.

1/5
You can observe this by eyeballing charts.

VIX (blue) stays within a range the whole time. If it gets to extreme values, it's likely to revert back to moderate values.

By contrast, the SPX price (orange) just seems to drift away. It doesn't appear anchored to any range.

2/5
We can also see this by sampling from the distributions at different times.

Let's divide our sample roughly in half (2004-2012 vs 2013-2021).

From the histogram, it's clear that SPX prices are not drawn from the same distribution in the first and second periods

3/5
Read 6 tweets
9 Mar
Let me try to be helpful.

If you have some kind of factor that you think predicts future stock returns (or similar) and you are making charts like below, then here are some tips...

We'll go through an example of trying to "time" SPX with the level of VIX.

A thread 👇👇👇👇
You get daily SPX index prices and daily VIX close data

You align them by date and plot them on dual axes, in true RealVision style.

"SPX tends to go down when VIX is high. I can therefore time an SPX allocation based on VIX. Let me share this on twitter" you say.

No. Very no.
There are two main problems with what you did:

1. The SPX price drifts. We can't directly compare the price of SPX in 2004 with its price in 2021

2. As traders, we are more interested in whether high VIX is *followed by* decreasing SPX prices, not *coincident with* them.
Read 23 tweets

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