WEBVTT

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Hello,

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welcome back to Papers with Backtest podcast.

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Today we dive into another algo trading research paper.

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Indeed.

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The paper we're tackling today is a multi-strategy approach to trading foreign exchange futures.

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It's by Sonam Srivastava and colleagues from back in January 2019.

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Okay,

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FX futures.

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Yeah,

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that's a tricky market.

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What really caught my eye here is this idea,

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maybe instead of,

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you know,

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hunting for that one magic bullet indicator.

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Maybe combining different signals is actually the smarter way to go.

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So our mission today really is to get into the trading rules they looked at and critically what the back test showed.

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Does this multi-strategy thing actually work?

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That's exactly it.

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Yeah.

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The core argument is that blending indicators,

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you know,

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technical ones like momentum or mean reversion with something like FX carry.

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Well,

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it could potentially give you better,

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more consistent returns,

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more alpha,

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basically,

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than just trying to perfect a single approach.

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Right.

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So how did they actually test that?

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What was the process step by step?

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Well,

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they had a pretty structured methodology.

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First up,

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instrument selection.

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They focused on the big ones,

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the eight most liquid current month FX futures on the CME.

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Okay.

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Like the Euro Yen.

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Exactly.

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Australian dollar,

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British pound,

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Canadian dollar,

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Euro FX,

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Japanese Yen,

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Mexican peso,

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New Zealand dollar,

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and the Swiss franc.

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And they used a lot of data going back to

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1995. Wow.

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Okay.

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Yeah.

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And importantly,

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they used a T3 day rollover.

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That's just a way to handle the contract switching smoothly,

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avoid weird price jumps near expiry.

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Got it.

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Solid foundation.

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So what kinds of signals,

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what indicators did they build for these currencies?

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They built quite a few actually,

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and looked at both short-term,

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so three-month and long-term 12-month versions.

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First,

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there was interest rate carry.

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The classic carry trade logic.

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Pretty much.

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They measured it using the difference between long-term,

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that's 10-year,

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and short-term,

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one-year government bond yields.

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Specifically,

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the log difference.

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They call these short-term yield difference and long-term yield difference.

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Makes sense.

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Follow the yield.

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What else?

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Then standard technicals.

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Momentum,

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just looking at three-month and 12-month log returns.

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They label those short-term momentum and long-term momentum.

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Okay.

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Trend following.

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And the opposite,

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mean reversion,

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looking at returns relative to three-month and

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12-month moving averages.

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Again,

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short-term mean reversion and long-term mean reversion.

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Standard toolkit stuff.

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And I thought you mentioned linking to other markets,

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too.

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Yes,

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exactly.

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They brought in equity momentum.

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The idea here is that some currencies,

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especially commodity or emerging market ones,

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tend to follow their local stock markets,

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right?

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Yeah,

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that linkage is known.

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So they calculated three-month and

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12-month momentum for related indices,

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like the Aussie dollar linked to the ASX 200,

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the pound of the FTSE 100.

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You get the idea.

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Table one in the paper lists them all.

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These were the short-term equity momentum and long-term equity momentum signals.

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Interesting.

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Connecting the dots across assets.

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What about commodities themselves?

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Yep.

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Covered that too.

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Commodity momentum.

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They looked at the SPGSEI index,

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Brent crude,

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gold,

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and an agriculture index.

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Again,

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both short-term,

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three-month,

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and long-term,

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12-month versions.

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Short-term commodity momentum,

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long-term commodity momentum.

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Oh,

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okay.

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And one last one.

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Realized volatility.

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They created short and long term indicators based on how choppy the price action of these currency had been historically.

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Short term volatility,

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long term volatility.

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Right.

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So that's a pretty wide range of signals.

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How did they compare them fairly?

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They must operate on totally different scales.

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Good point.

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That's where normalization came in.

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They squashed all the raw indicator values into a standard range.

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Minus 0.5 to plus 0.5.

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Ah,

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OK.

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They use a walk forward percentile method.

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Basically,

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it adjusts the scaling based on recent history.

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So it adapts a bit.

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This meant they could apply consistent position sizing rules later.

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Right.

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You need that common scale.

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And the sizing,

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how much did they bet on each signal?

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They used risk budgeting.

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The goal was to aim for 10%

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annualized volatility for each individual indicator strategy.

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And interestingly,

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they allowed negative allocations.

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Meaning they could go short based on a signal.

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Exactly.

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Not just long signals.

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Okay.

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So now we have all these individual strategies normalized with risk targets.

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This is the core of it,

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right?

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How did they combine them?

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Yes,

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this was a major part of their investigation.

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They tried a whole bunch of different combination methods.

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Simple stuff like equal weighting.

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Just give each strategy the same slice of the pie.

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Okay,

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easy enough.

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And more complex things like risk parity,

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where you allocate so each strategy contributes the same amount of risk.

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Or weighting by Sharpe ratio,

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giving more weight to historically better performers.

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With or without considering correlation?

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Both.

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They had versions proportional to Sharpe.

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and a correlation-aware sharp weighting.

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Plus,

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optimization methods like trying to maximize diversification,

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basically,

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picking weights to make the combined strategies as uncorrelated as possible.

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Trying to smooth out the ride.

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Exactly.

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And a couple of others aimed at maximizing sharp directly,

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or even maximizing the 10th percentile of the rolling one-year sharp.

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A more conservative optimization.

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Okay,

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a lot of ways to mix and match.

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So first things first,

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how did those individual strategies perform on their own?

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Any standouts?

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Yeah,

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if you look at their table three,

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the results are pretty clear.

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The single best performer,

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by quite a margin actually,

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was the long term yield difference strategy.

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Highest return,

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highest sharp.

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Huh,

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just following the long term rate differentials.

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Interesting.

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Short term commodity momentum and short term yield difference also did okay on their own.

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And the losers,

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any single strategies that just didn't work well?

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Well,

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according to their back tests,

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yeah.

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Short-term mean reversion,

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long-term volatility,

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and long-term equity momentum.

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They didn't really deliver strong risk-adjusted returns individually during this period.

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Good to know.

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Okay,

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now for the main event.

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What happened when they started combining these using those different methods,

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starting with the simple uniform weights across all currencies?

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Right,

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so in this first combination approach,

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the same weighting rule applied to all eight currencies.

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Looking at Table 4 and Figure 3,

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the results were,

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well,

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quite encouraging for the multi-strategy idea.

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The best combination methods significantly outperformed the best single strategy.

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The top performer was actually the one optimizing for the 10th percentile of the rolling sharp.

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Ah,

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the more conservative optimization paid off.

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It seemed so.

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Volatility-scaled negative correlation and maximum diversification also did pretty well.

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It really showed that combining things added value

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beyond just picking the best individual signal.

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That's a really key finding.

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But you mentioned some combination methods weren't great.

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Yes,

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they specifically called out that simply optimizing to maximize the overall Sharpe ratio didn't perform that well.

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Ah,

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the classic overfitting trap.

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Looks like it.

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It suggests just chasing the highest historical Sharpe can lead you down a bad path.

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Focusing on consistency,

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like that tenth percentile method or diversification,

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seems more robust.

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That makes a lot of sense.

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Don't just fit the noise.

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OK,

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so then they took it even further,

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right?

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Allowing different indicators for different currencies.

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Exactly.

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The product X indicator combinations.

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This was more complex.

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They basically treated each currency indicator pair like

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Aussie dollar long term yield difference as its own mini strategy.

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Whoa,

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that massively increases the number of things to combine.

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It does.

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They did filter out some of the really poorly performing individual pairs first,

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heuristically,

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before combining the rest.

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Table five shows some examples like.

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Long-term yield difference was useful for several currencies,

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but maybe equity momentum only for specific ones.

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And the results?

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Did this more granular approach boost performance again?

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It really did.

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Table 6 and Figure 6 show another jump in performance.

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Again,

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maximizing diversification and maximizing the 10th percentile of sharp were standouts.

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How much better are we talking?

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Significantly better.

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The sharp ratio for the best combination here was roughly 60%

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higher.

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than the sharp of the best single indicator strategy.

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Wow.

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A 60%

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improvement is huge.

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That really drives home the point,

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doesn't it?

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Absolutely.

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It strongly suggests that different factors matter more for different currencies.

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A one-size-fits-all approach,

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even in combination,

286
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isn't necessarily optimal.

287
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Tailoring the indicator mix per currency pair paid off,

288
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at least in this backtest.

289
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So if we boil it all down,

290
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what's the big takeaway for someone trading FX systematically?

291
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I think the core message is pretty clear.

292
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Don't just look for the single best signal.

293
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A multi-strategy approach where you thoughtfully combine diverse indicators carry,

294
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momentum,

295
00:08:32.128 --> 00:08:32.469
vol-vol,

296
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maybe even cross asset links using robust methods,

297
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well,

298
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it has the potential to seriously improve your risk-adjusted returns.

299
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And robust methods seems key there,

300
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focusing on diversification or consistency,

301
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not just chasing peak historical sharp.

302
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Precisely.

303
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Methods like maximizing diversification or optimizing for that lower percentile of rolling sharp.

304
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seem particularly effective here.

305
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It's about building a diversified portfolio of strategies,

306
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not relying on one potentially fragile edge.

307
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That definitely shifts the perspective.

308
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It's less about finding the silver bullet and more about building a robust toolkit.

309
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Exactly.

310
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Thinking about how different market dynamics interact and capturing them systematically.

311
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It's definitely food for thought.

312
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The researchers even mentioned looking deeper into why certain factors work for certain currencies,

313
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maybe bringing in macro data too.

314
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Lots more to explore there.

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Thank you for tuning in to Papers with Backtests podcast.

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We hope today's episode gave you useful insights.

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Join us next time as we break down more research.

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And for more papers and backtests,

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find us at https.paperswithbacktests.com.

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Happy trading.