It’s really sad that someone smart and enthusiastic, who was openly learning in public, giving her work away free and using subscriptions to raise money for charity, feels like she can’t be on Twitter any more.
Was Lily right about everything she posted? No, but who cares? Being willing to think about markets from new angles and put it out there, opening yourself up to mistakes and criticism, is incredibly brave and we should encourage more of that, not less.
Let’s not shy away from the fact that Lily came in for extra criticism because she’s a woman. I see far worse takes from guys with bigger followings every day and they don’t get anywhere near the same volume or the same kind of criticism.
It’s particularly bad when the snipes and insults come from guys who have worked in finance for years and have already built their careers. Have some respect and understanding for someone who is enthusiastic and learning, you fucking assholes.

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More from @macrocephalopod

20 Apr
Since lumber is in the news, seems like a good time to revisit one of my favorite trading anomalies -- front month lumber futures returns by day of week. Image
Hey @LumberTrading do you know why this happens?
Fuck, I really want an explanation for this now. Lumber futures go up Thu/Fri and down Mon/Tue on average. Any ideas @LumberNews @THELumberGuru @LumberBS @forest2market @TreeFrogNews?
Read 4 tweets
14 Apr
My advice here was pretty standard - profile, find bottlenecks, use caching/vectorization/numpy/numba to speed up the hot code.

But even better advice is "do less backtesting" (as pointed out by @therobotjames)
Backtesting is not a research tool. It belong right at the end of the process and you use it for four things -

1. Final verification of your idea
2. Sensitivity to assumptions
3. Reconciliation vs live trading
4. Non-performance stats like turnover, max net/gross exposure etc
Only in the first of these do you care about performance, and only then to check that the simulation passes a "good enough" bar. Using the backtest to optimize parameters is the path to overfitting.
Read 10 tweets
6 Apr
Useful equation to remember when thinking about leverage, forced trading and impact on prices is X = L * (L - 1) * R.

Here R is the return on the underlying stock/futures/whatever, L is leverage and X is the trade required to maintain constant leverage after a price move.
To get there, imagine a portfolio L:1 leverage i.e. you have $L of positions for every $1 of equity.

If the stock has a return of R, you now have equity of (1 + LR) and stock worth L(1+R)
To maintain leverage of L you need the stock you hold to be worth L(1+LR) so you need to buy/sell

X = L(1+LR) - L(1+R) = L + L^2R - L - LR = L(L-1)R

Remember we are doing this per $1 of equity, so multiply by your initial account size to get a $ trade size.
Read 8 tweets
6 Apr
This did some numbers over the weekend and was rightly mocked. Source of the claim was this chart in the FT showing an estimate of the size of Archegos' portfolio, which shows a drop of over $100 billion in March. Source of the chart is Bespoke Investment Group.
Obviously Archegos did not lose anywhere near $110 billion - they lost something like $10-15 billion plus maybe another $7-10 billion lost by prime brokers (CS, Nomura etc).

So did the FT or Bespoke fuck up?
No! Pink line on the chart shows an estimate of Archegos' portfolio size (reconstructed from bank 13-F filings) assuming *constant 5:1 leverage*

The constant leverage is important as it means that portfolio size grows/shrinks very quickly even when gains/losses are modest.
Read 10 tweets
3 Apr
This is a nice question -- roughly "What does the futures curve look like for quarterly futures that settle into quarterly GDP % growth estimates"?
Quarterly GDP numbers are not tradable which means there is no cash and carry arbitrage. If you can't trade spot then futures will be priced on three things --

1. Expectations
2. Seasonality
3. Risk premium
1. and 2. are kind of the same thing, basically "what is your best guess of GDP prints for the next three years?" This depends on long term expectations, seasonal fluctuations, and other factors.
Read 16 tweets
31 Mar
Article seems to be wrong on several major points?

• "The fund was not overly levered"
• "Its risk was not hidden"
• "Hwang typically ran dollar-neutral portfolios"
• "Analysts had full insight into Archegos’ ... position sizes"
bloomberg.com/opinion/articl…
Like given the fund went from $10-15bn to ~$0 in like a week, without a major event or liquidity crisis, I would probably say it was overly levered?
There is a *massive* difference between a quant equity book (carefully hedged to sector, country and factor exposures) which can run 10-15x leverage and still be pretty low volatility vs. levered 5x long in concentrated shitcos "hedged" with index futures
Read 7 tweets

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