Polar Star Quantitative Commodity Fund

Dr Mauritz van den Worm, PhD

22 August, 2019

Manual vs Automated Trading

How has the increase of Automated Trading Systems (ATS) influenced the futures market?

  • CME transaction data identitifies ATS with the 1028 tag
  • Data available from November 2012 to present
  • Consider data form 2012 to 2016
  • Use gradient plots to highlight the changes
  • Graphs are interactive

MAN vs ATS - Group

MAN vs ATS - Agriculture

MAN vs ATS - Energy

Trade Participation

Where are the opportunities?

  • Limited, Local, Spectrum
    • Less liquid parts of the curve
    • Changes to macro
    • Concentrated risk on asymmetric risk/reward trades


  • Quantitative
    • Harvest systematic alpha from large universe of commodities
    • Rule based decision making process
    • Strategies inspired by discretional methodology

Literature

Grinold’s Fundamental Law of Active Portfolio Management

Law of active portfolio management

\[ \text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}} \]

Suppose we are in a coin flipping casino

  • Flip coins in stead of futures
  • The coin is biased - \(P(\text{heads}) = 0.51\)

How does the betting work?

  • We have 1000 coins
  • The minimum wager is 1 coin
  • If you win you gain 1 coin
  • If you loose you loose 1 coin
  • There are 1000 tables with coin wagers
  • Games runs in parallel

What is the optimal way to allocate coins?

Two extremes

  • Bet 1000 coins on one coin flip
  • Bet 1 coin on 1000 coin flips

Expected Return

  • Single bet: \(0.51 \times 1000 + 0.49 \times (-1000) = 20\)

  • Multi bet: \(1000 \times [0.51 + 0.49 \times (-1)] = 20\)

The same expected return

Risk - Probability to lose it all:

  • Single bet: 49%

  • Multi bet: \(0.49 \times 0.49 \times \dots \times 0.49 = 0.49^{1000} \approx 0\)

Risk - Standard Deviation:

  • One coin per table

\[ \text{risk} := \text{std}\left\{1,-1,-1,1, \dots, 1 \right\} = 1 \]

  • One 1000 coin bet, 999 zero coin bets

\[ \begin{align} \text{risk} &:= \text{std}\left\{1000,0,0,0, \dots, 0 \right\} = 31.62 \\ \text{risk} &:= \text{std}\left\{-1000,0,0,0, \dots, 0 \right\} = 31.62 \end{align} \]

Coin Flip Casino - Reward/Risk

  • Just like Sharpe Ratio

  • Single bet: \(\text{SR}_{\text{single}} = \frac{20}{31.62} =0.63\)

  • Multi bet: \(\text{SR}_{\text{multiple}} = \frac{20}{1} =20\)

Coin flipping casino - Observation

  • \(20 = 0.63 \times \sqrt{1000}\)

  • \(\text{SR}_{\text{multiple}} = \text{SR}_{\text{single}} \times \sqrt{\text{Bets}}\)

  • \(\text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}}\)

How does this apply to commodity futures?

  • We use insights gained from years of fundamental trading to inspire bespoke quantitative strategies that are applied to a large collection of commodity markets

  • We increase breadth or diversification by
    • how,
    • what and
    • when we trade

When we trade

What we trade

Literature

Technical considerations when trading futures systematically

  • Continuous Futures Price Series
    • We require long time series data
    • Futures expire too soon to gather sufficient data
    • How do you handle rolls?


  • Non-stationarity of Price Data
    • Time series data can only reliably be forecasted if stationary
    • Machine Learning algorithms are designed for stationary features
    • How do we create stationary data?

Continuous Futures Price Series