- Portfolio manager background
- Cyclic nature of commodity markets
- Long term drivers of commodity prices
- Philosophy of taking many small bets
- The core strategies
- Portfolio

PhD in Quantum Physics from Stellenbosch University - 2015

Applications:

- Rapid information transfer in quantum systems
- Spreading of correlations in quantum systems
- Theory of trapped ion quantum simulators
- Wishlist: Development of quantum computers

- Breakdown of Quasilocality in Long-Range Quantum Lattice Models
- J Eisert, M van den Worm, SR Manmana and M Kastner
- Physical Review Letters 111 (26), 260401
- Cited by 132

- Relaxation timescales and decay of correlations in a long-range interacting quantum simulator
- M van den Worm, BC Sawyer, JJ Bollinger and M Mastner
- New Journal of Physics 15, 083007
- Cited by 101

- Quantum correlations and entanglement in far-from-equilibrium spin systems
- KRA Hazzard, Mvan den Worm, et al.
- Physical Review A 90 (6), 063622
- Cited by 87

- Each commodity has a different curve structure
- You can think of the curve structure as a tide
- It is much easier to swim with the tide than against it
- For many commodities the curve structure has cyclical behaviour
- Using Curve Carry we try to exploit this
- Using the curve structure in a trend strategy helps in entering profitable long term trades

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

**Skill**measures your ability to find profitable opportunities**Breadth**captures how many opportunities you have available

- We know the coin is biased with \(P(\text{heads}) = 0.51\)
- 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

How do we place the bets?

Two extremes:

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

The same expected return:

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

Totally different risk to loose it all:

- Single bet: 49%
- Multi bet: \(0.49 \times 0.49 \times \dots \times 0.49 = 0.49^{1000} \approx 0\)

Another way to look at risk is to use standard deviation of returns

- Multi bet:

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

- Single bet:

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

Risk Adjusted Returns (Sharpe Ratio):

- Single bet: \(\text{SR}_{\text{single}} = \frac{20}{31.62} =0.63\)
- Multi bet: \(\text{SR}_{\text{multiple}} = \frac{20}{1} =20\)

We rewrite the risk adjusted return as:

- \(20 = 0.63 \times \sqrt{1000}\)
- \(\text{SR}_{\text{multiple}} = \text{SR}_{\text{single}} \times \sqrt{\text{Bets}}\)
- \(\text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}}\)

**What**you trade- Different commodities
- Different calendar spreads
- Different parts of the curve

**How**you trade- Trend
- Curve Carry

**When**you trade- Different signals
- Different times of the year
- Roll methodology and timing

All of these add to the **breadth** of opportunities.

Aim of the strategy:

- Harness the curve carry
- Storable commodities have predictable cuve structure
- Higher stock levels are associated with contango curves
- Many commodities evolve into a contango as the front nears expiry

We define \(p\) as the ratio of the Front to the Deferred price \[ p := \text{Front}/\text{Deferred} \]

- When \(p\) < 1 the Front is trading at a discount to the Deferred.
- The smaller \(p\) the greater the contango

From a fundamental point of view we

- Identify commodity calendar spread pairs
- With predictable behaviour during certain parts of the year

For every month of the year we have a list of

- Commodities,
- Associated calendar spread we want to be involved with and
- The side of the trade

ML Technique to determine probability of the spread ending in the money

Size positions according to the

- Volatility and
- Probability of profit
- Lower volatility receives a higher allocation

Risk/Reward Statistics | |
---|---|

Sharpe Ratio | 1.16 |

Sortino Ratio | 2.04 |

Omega Ratio | 1.56 |

Skewness | 0.61 |

Kurtosis | 2.50 |

Risk Statistics | |
---|---|

Annualized Std.Deviation | 17.70 |

Maximum Drawdown | 27.81 |

Month to Recover | 23.00 |

Worst Month | -14.23 |

Losing Months (%) | 37.45 |

Average Losing Month | -2.91 |

Loss Deviation | 2.90 |

Return Statistics | |
---|---|

Last Month | 3.35 |

Year To Date | 13.33 |

3 Month ROR | 13.33 |

12 Month ROR | 14.65 |

36 Month ROR | 73.80 |

Total Return | 4291.44 |

Compound ROR | 20.54 |

Best Month | 20.74 |

Winning Months (%) | 62.55 |

Time Window Analysis:

1 Month | 3 Month | 6 Month | 1 Year | 2 Year | 3 Year | 5 Year | |
---|---|---|---|---|---|---|---|

Best | 20.74 | 42.58 | 96.52 | 153.25 | 268.48 | 450.79 | 754.88 |

Worst | -14.23 | -27.81 | -19.57 | -24.12 | -10.67 | -5.15 | 10.39 |

Average | 1.69 | 5.17 | 10.73 | 23.88 | 59.19 | 107.30 | 263.66 |

Median | 1.11 | 3.82 | 8.42 | 16.86 | 37.87 | 58.54 | 136.32 |

Last | 3.35 | 13.33 | 12.92 | 14.65 | 35.91 | 73.80 | 88.74 |

Winning (%) | 62.55 | 73.03 | 81.09 | 86.21 | 87.27 | 99.52 | 100.00 |

Avg. Pos. Period | 4.45 | 8.69 | 14.81 | 29.31 | 68.31 | 107.85 | 263.66 |

Avg. Neg. Period | -2.91 | -4.34 | -6.81 | -10.11 | -3.33 | -5.15 | NaN |

# Of Periods | 243.00 | 241.00 | 238.00 | 232.00 | 220.00 | 208.00 | 184.00 |

Drawdown Report:

Depth (%) | Length (Months) | Recovery (Months) | Start | End |
---|---|---|---|---|

-27.81 | 23 | 20 | 2014-01-31 | 2015-11-30 |

-24.31 | 23 | 19 | 2003-08-29 | 2005-06-30 |

-15.75 | 27 | 18 | 2000-06-30 | 2002-08-30 |

-9.83 | 3 | 2 | 2007-07-31 | 2007-09-28 |

-9.38 | 7 | 3 | 2013-06-28 | 2013-12-31 |

Aim of the strategy:

- Trade medium to long term commodity trends profitably
- Lookback windows ranging from a couple of weeks to one year
- Breakout, Donchain and Exponential Moving averge signals
- Target volatility 20%
- Diverse universe of commodities
- Multiple parts of the futures curves
- Roll structure created from fundamental understanding

Essence: *Cap your losses and let the winners run*

Build the strategy on fake data:

- Create data with trends
- See if we can trade it profitably
- Investigate price behaviour that cannot be traded profitably

Why is this preferred:

- No overfitting of the underlying market data
- Datamined lookbacks and models perform great in sample
- Majestic performance is difficult to follow up on new data

Risk/Reward Statistics | |
---|---|

Sharpe Ratio | 1.07 |

Sortino Ratio | 2.12 |

Omega Ratio | 1.43 |

Skewness | 1.14 |

Kurtosis | 6.27 |

Risk Statistics | |
---|---|

Annualized Std.Deviation | 21.13 |

Maximum Drawdown | 34.99 |

Month to Recover | 42.00 |

Worst Month | -21.87 |

Losing Months (%) | 39.63 |

Average Losing Month | -3.33 |

Loss Deviation | 3.09 |

Return Statistics | |
---|---|

Last Month | 4.88 |

Year To Date | 9.13 |

3 Month ROR | 9.13 |

12 Month ROR | 2.46 |

36 Month ROR | 2.14 |

Total Return | 432495.07 |

Compound ROR | 22.65 |

Best Month | 44.67 |

Winning Months (%) | 59.96 |

Time Window Analysis:

1 Month | 3 Month | 6 Month | 1 Year | 2 Year | 3 Year | 5 Year | |
---|---|---|---|---|---|---|---|

Best | 44.67 | 88.63 | 114.33 | 142.94 | 238.83 | 341.48 | 643.48 |

Worst | -21.87 | -17.48 | -19.76 | -21.19 | -30.54 | -27.88 | -5.06 |

Average | 1.89 | 5.78 | 12.02 | 25.94 | 59.54 | 101.99 | 215.38 |

Median | 1.19 | 4.31 | 8.83 | 21.68 | 52.27 | 96.27 | 217.82 |

Last | 4.88 | 9.13 | 7.17 | 2.46 | -0.61 | 2.14 | -4.06 |

Winning (%) | 59.96 | 67.55 | 75.56 | 82.74 | 89.55 | 92.34 | 98.61 |

Avg. Pos. Period | 5.36 | 10.88 | 17.97 | 32.93 | 67.76 | 111.36 | 218.45 |

Avg. Neg. Period | -3.33 | -4.84 | -6.39 | -7.55 | -10.91 | -10.95 | -3.27 |

# Of Periods | 492.00 | 490.00 | 487.00 | 481.00 | 469.00 | 457.00 | 433.00 |

Drawdown Report:

Depth (%) | Length (Months) | Recovery (Months) | Start | End |
---|---|---|---|---|

-34.99 | 42 | 9 | 2011-03-31 | 2014-08-29 |

-25.58 | 50 | NA | 2016-03-31 | NA |

-21.87 | 7 | 6 | 1980-12-31 | 1981-06-30 |

-17.83 | 16 | 4 | 2004-08-31 | 2005-11-30 |

-16.15 | 7 | 1 | 2008-03-31 | 2008-09-30 |

Risk/Reward Statistics | |
---|---|

Sharpe Ratio | 1.09 |

Sortino Ratio | 2.19 |

Omega Ratio | 1.39 |

Skewness | 0.74 |

Kurtosis | 2.52 |

Risk Statistics | |
---|---|

Annualized Std.Deviation | 15.03 |

Maximum Drawdown | 19.62 |

Month to Recover | 15.00 |

Worst Month | -9.75 |

Losing Months (%) | 40.08 |

Average Losing Month | -2.45 |

Loss Deviation | 2.17 |

Return Statistics | |
---|---|

Last Month | 5.38 |

Year To Date | 14.99 |

3 Month ROR | 14.99 |

12 Month ROR | 13.65 |

36 Month ROR | 43.38 |

Total Return | 2048.96 |

Compound ROR | 16.43 |

Best Month | 20.01 |

Winning Months (%) | 59.92 |

Time Window Analysis:

1 Month | 3 Month | 6 Month | 1 Year | 2 Year | 3 Year | 5 Year | |
---|---|---|---|---|---|---|---|

Best | 20.01 | 46.51 | 81.33 | 100.70 | 165.95 | 296.47 | 438.92 |

Worst | -9.75 | -18.29 | -13.78 | -16.37 | -7.71 | 9.41 | 23.01 |

Average | 1.37 | 4.10 | 8.31 | 17.77 | 41.26 | 71.46 | 162.49 |

Median | 0.83 | 3.13 | 5.71 | 13.65 | 25.89 | 35.89 | 96.92 |

Last | 5.38 | 14.99 | 14.19 | 13.65 | 20.08 | 43.38 | 46.22 |

Winning (%) | 59.92 | 70.00 | 81.86 | 87.45 | 98.63 | 100.00 | 100.00 |

Avg. Pos. Period | 3.92 | 7.27 | 11.10 | 21.02 | 41.89 | 71.46 | 162.49 |

Avg. Neg. Period | -2.45 | -3.29 | -4.29 | -4.82 | -4.46 | NaN | NaN |

# Of Periods | 242.00 | 240.00 | 237.00 | 231.00 | 219.00 | 207.00 | 183.00 |

Drawdown Report:

Depth (%) | Length (Months) | Recovery (Months) | Start | End |
---|---|---|---|---|

-19.62 | 15 | 5 | 2013-06-28 | 2014-08-29 |

-17.19 | 15 | 11 | 2003-08-29 | 2004-10-29 |

-11.39 | 5 | 3 | 2008-06-30 | 2008-10-31 |

-9.75 | 6 | 5 | 2006-06-30 | 2006-11-30 |

-9.26 | 4 | 2 | 2010-06-30 | 2010-09-30 |

- Long term positive returns
- Exploit fundamental knowledge and curve structure
- Diversified quantitative commodity product
- Low correlation to
- other Polar Star funds,
- equities and
- commodity indices