Fully automated futures trading
- Thread starter globalarbtrader
- Start date
This thread is really interesting. Thank you for sharing your experience. Your book is a great read as well.
One question, at what point will margin calls become an issue on your portfolio? Would this only happen if you have a 100% drawdown or could you be subject to this sooner? If so how do you manage the risk of a margin call?
One question, at what point will margin calls become an issue on your portfolio? Would this only happen if you have a 100% drawdown or could you be subject to this sooner? If so how do you manage the risk of a margin call?
Things are confused by the fact that I have both stock and cash funding my portfolio, and because I have 'too much' in my account. Let's pretend that I had 100% cash funding:
Balance sheet
Account value: 390K
Cash: 390K
Margin: 120K (31% of available cash)
Capital at risk is 390K (reflecting a 2.5% drawdown on maximum capital at risk of 400K)
Now let's suppose I have an 'extreme' level of risk; targeting twice my long run average expected risk of 25% x 390K = 97.5K a year, or 6.1K a day. In this case I'd probably be using about 195K of margin (pro-rata based on what my current expected risk is)
Account value: 390K
Cash: 390K
Margin: 195K (50% of available cash)
Let's take an extreme day when I lose 10 sigma on twice my average risk; or 122K (a 31% one day loss); in a 'gap' when I don't have the chance to sell down any positions.
That would reduce my cash and capital at risk 390 - 122 = 268K,
Account value: 268K
Cash: 268K
Margin: 195K (72% of available cash)
However at the first opportunity my positions would reduce by 1 - 268/390 = 31%, and my margin usage would fall by 31% to 134K (back to 50% of cash)
(It's likely that my positions would reduce further due to elevated volatility and trends being reversed, but it's hard to quantify what effect this would have, so I'm going to ignore this effect).
Average long run expected risk has also fallen by 31% to 4.2K.
Account value: 268K
Cash: 268K
Margin: 134K (50% of available cash)
Suppose we have another 10 sigma day and lose 10 x 2 x 4.2 = 84K.
Cash and capital at risk to 268 - 84 = 184K
Account value: 184K
Cash: 184K
Margin: 134K (72% of available cash)
Again then positions would reduce by 1 - 184/268 = 31%, margin usage to 92K (again, 50% of cash).
Average expected risk is 2.9K a day.
Account value: 184K
Cash: 184K
Margin: 92K (50% of available cash)
Another 10 sigma day; lose 57K:
Account value: 127K
Cash: 127K
Margin: 92K (72% of available cash)
Again positions reduce by 31%, margin usage falls to 63K (50% of capital).
Basically because I'm reducing my positions proporportionally as I lose money I will always be using 50% of my remaining cash for margin. So I should never be in a position where a margin call causes me problems; unless I get hit by an extremely large gap.
For example if at any point I lost more than 40% before I got a chance to reduce my positions, then I'd have some forced liquidation.
My maximum expected risk (at double the long run average) is 50% a year, or 3.1% a day. That means I'd need to see a 13 sigma day before hitting a margin problem. I'm comfortable that is pretty unlikely. Not as unlikely as the gaussian distribution suggests (incalculably small); October 87 was a 20 sigma event but with a large diversified portfolio twenty sigma is pretty unlikely and even 13 sigma should be fairly rare.
But suppose I was aiming for a much higher risk; and/or had a strategy that consumed more margin. Imagine you had 90% of your capital used for margin; and you were targeting 100% annualised volatilty. Then a mere 1.6 sigma event would wipe you out....
GAT
Update (last update was 22nd september). I'm up about 7% of capital, or 28K. I also hit a new HWM on Friday; which was nice.
Current drawdown is 3.8% (off new HWM).
I'm also back above the key pyschological point where I have made more money than I have at risk (400K; well actually 96.2% of 400K to be pendantic due to the DD). It's interesting to speculate about these key points. For example once I made 300K I had doubled my starting capital. If I make another 45K or so this year I'll be at 100K for the year; which would be a Sharpe Ratio for the year of 1.0 (although the actual figure could be different depending on the exact monthly/weekly/daily ups and downs). If I lose 185K then I will be in a 50% drawdown.
Do these points have more effect on discretionary trader behaviour than key price points? It's an interesting point, but it's not obvious how we would model them and use them in a predictive sense.
I just did my 6 monthly accounts. My net worth is pretty much unchanged; to put it another way the profits from my futures trading covered the losses in my long only stock and bond portfolio, with enough left over to pay for my living costs. Trend following is a nice hedge (as discussed here).
I also just bought a new backup machine (intense pc) which is proving to be fantastic. I would have bought another mint box but they seem to have run out of UK stock. This is pretty much identical; so now both my machines are of similar spec (before ) and I can swap them more frequently which is obviously safer.
There are some new interviews here and a couple of new book reviews here. (near the bottom of each page)
Gainers:
Crude +6.1K
Plat 3.2K
Pallad 2.2K
JPU 1.2K
KOSPI 1.4K
Losers:
SP500 -1.8K
KR3 -1.2K
Livecow -1.1K
Wheat -1K
Positions:
Risk:
It's interesting how few longs I have nowadays. Right now isn't a great time to be a long only investor.
Trades:
Slippage: £402 vs expectations of £208
Ouch. I had a bad AUD and GBP trade which caused most of the problems here. Looking at the chart it looks as though the price I had when I submitted the order was stale. So it's bad data rather than a gap. Hopefully these should even out over the year so I don't have to go through the effort of manually cleaning them out of the database. Due to latency these figures can only ever be an estimate.
GAT
Current drawdown is 3.8% (off new HWM).
I'm also back above the key pyschological point where I have made more money than I have at risk (400K; well actually 96.2% of 400K to be pendantic due to the DD). It's interesting to speculate about these key points. For example once I made 300K I had doubled my starting capital. If I make another 45K or so this year I'll be at 100K for the year; which would be a Sharpe Ratio for the year of 1.0 (although the actual figure could be different depending on the exact monthly/weekly/daily ups and downs). If I lose 185K then I will be in a 50% drawdown.
Do these points have more effect on discretionary trader behaviour than key price points? It's an interesting point, but it's not obvious how we would model them and use them in a predictive sense.
I just did my 6 monthly accounts. My net worth is pretty much unchanged; to put it another way the profits from my futures trading covered the losses in my long only stock and bond portfolio, with enough left over to pay for my living costs. Trend following is a nice hedge (as discussed here).
I also just bought a new backup machine (intense pc) which is proving to be fantastic. I would have bought another mint box but they seem to have run out of UK stock. This is pretty much identical; so now both my machines are of similar spec (before ) and I can swap them more frequently which is obviously safer.
There are some new interviews here and a couple of new book reviews here. (near the bottom of each page)
Gainers:
Crude +6.1K
Plat 3.2K
Pallad 2.2K
JPU 1.2K
KOSPI 1.4K
Losers:
SP500 -1.8K
KR3 -1.2K
Livecow -1.1K
Wheat -1K
Positions:
Code:
code contractid positions Lock WrongContract InFwdNotRoll
0 AUD 201512 -1 False False False
6 BOBL 201512 4 False False False
25 BTP 201512 3 False False False
4 BUND 201512 1 False False False
18 COPPER 201512 -2 False False False
17 CORN 201612 -2 False False False
11 CRUDE_W 201512 -2 False False False
16 EDOLLAR 201903 3 False False False
23 EDOLLAR 201812 10 False False False
20 EUR 201512 -1 False False False
24 EUROSTX 201512 -13 False False False
3 GAS_US 201512 -5 False False False
9 GBP 201512 -3 False False False
10 GOLD 201512 -1 False False False
15 KR10 201512 2 False False False
13 KR3 201512 9 False False False
1 LEANHOG 201606 3 False False False
21 LIVECOW 201610 -2 False False False
8 MXP 201512 -5 False False False
14 NZD 201512 -2 False False False
19 OAT 201512 1 False False False
27 PLAT 201601 -3 False False False
2 US10 201512 1 False False False
12 US2 201512 2 False False False
22 US5 201512 2 False False False
5 V2X 201512 1 False False False
26 V2X 201511 3 False False False
7 WHEAT 201612 -2 False False FalseRisk:
Code:
Expected annual risk more than GBP6400 per year, GBP400 per day
code multisignal expected_annual_risk expected_annual_risk_per_contract position expected_annual_risk_rounded_pos
28 MXP -15.3 10088 2248 -5 11242
25 EUR -12.8 8474 11307 -1 11307
31 GOLD -10.4 6843 11624 -1 11624
2 LIVECOW -19.5 12895 5846 -2 11692
29 NZD -15.4 10159 5967 -2 11934
26 GBP -22.6 14895 4841 -3 14524
30 COPPER -33.7 22256 11268 -2 22537
33 PLAT -31.9 21068 7569 -3 22706
35 GAS_US -37.3 24661 4823 -5 24113
34 CRUDE_W -32.4 21413 12360 -2 24721
5 KR10 10.6 7012 3156 2 6312
6 KR3 9.8 6497 725 9 6529
7 BOBL 9.8 6501 1767 4 7069
10 OAT 13.7 9068 7319 1 7319
1 LEANHOG 12.9 8486 2727 3 8180
36 EDOLLAR 29.4 19423 1456 13 18929
8 BTP 31.0 20465 6558 3 19674It's interesting how few longs I have nowadays. Right now isn't a great time to be a long only investor.
Trades:
Code:
code contractid filled_datetime filledtrade filledprice
5527 AUD 201512 2015-09-24 07:59:12 -1 0.695300
5590 BOBL 201512 2015-09-29 07:35:46 1 129.100000
5533 BTP 201512 2015-09-24 08:22:20 1 135.840000
5716 BUND 201512 2015-10-05 07:35:34 1 157.130000
5536 CAC 201510 2015-09-24 08:24:34 -1 4410.000000
5695 CAC 201510 2015-10-01 08:04:58 1 4507.500000
5470 COPPER 201512 2015-09-18 16:07:57 1 2.383500
5539 COPPER 201512 2015-09-24 12:01:34 -1 2.296000
5626 CORN 201612 2015-09-30 15:05:08 1 413.000000
5710 CRUDE_W 201512 2015-10-01 16:58:30 -1 45.740000
5461 EDOLLAR 201812 2015-09-18 12:13:41 -1 97.970000
5485 EUR 201512 2015-09-23 01:11:12 -1 1.113100
5473 GAS_US 201512 2015-09-18 18:18:02 -1 2.842000
5608 GAS_US 201511 2015-09-30 12:06:30 2 2.588000
5611 GAS_US 201512 2015-09-30 12:06:30 -2 2.763000
5614 GAS_US 201511 2015-09-30 12:17:48 2 2.581000
5617 GAS_US 201512 2015-09-30 12:17:48 -2 2.757000
5452 GBP 201512 2015-09-18 01:52:52 1 1.556400
5488 GBP 201512 2015-09-23 05:45:36 -1 1.534100
5560 GBP 201512 2015-09-25 02:05:46 -1 1.521400
5587 GBP 201512 2015-09-29 06:27:38 -1 1.516100
5458 GOLD 201512 2015-09-18 12:05:03 1 1136.500000
5476 GOLD 201512 2015-09-21 12:04:49 1 1137.100000
5701 GOLD 201512 2015-10-01 12:10:14 -1 1112.300000
5449 KR3 201512 2015-09-18 01:45:36 -2 109.620000
5455 KR3 201512 2015-09-18 04:49:37 -1 109.650000
5596 KR3 201512 2015-09-30 01:53:55 1 109.830000
5692 KR3 201512 2015-10-01 03:55:49 -1 109.830000
5584 LEANHOG 201606 2015-09-28 15:43:30 1 79.675000
5479 LIVECOW 201610 2015-09-21 16:14:37 -1 131.375000
5491 OAT 201512 2015-09-23 07:08:36 1 151.860000
5542 PALLAD 201512 2015-09-24 12:02:21 1 645.100000
5602 PLAT 201510 2015-09-30 12:02:45 1 925.500000
5605 PLAT 201601 2015-09-30 12:02:45 -1 926.300000
5620 PLAT 201510 2015-09-30 12:23:13 1 923.700000
5623 PLAT 201601 2015-09-30 12:23:13 -1 924.500000
5707 PLAT 201601 2015-10-01 16:54:55 -1 910.200000
5593 SMI 201512 2015-09-29 08:07:13 -1 8245.000000
5599 SMI 201512 2015-09-30 11:25:00 1 8484.000000
5464 SOYBEAN 201611 2015-09-18 12:30:07 1 882.000000
5557 SOYBEAN 201611 2015-09-24 12:15:37 -1 872.750000
5581 SOYBEAN 201611 2015-09-28 12:29:13 1 891.250000
5530 US10 201512 2015-09-24 06:53:05 1 128.031250
5467 US2 201512 2015-09-18 15:54:06 -1 109.382812
5704 US2 201512 2015-10-01 14:12:12 1 109.531250
5713 US2 201512 2015-10-02 15:13:50 -1 109.703125
5629 US5 201512 2015-09-30 15:15:53 1 120.468750
5698 V2X 201512 2015-10-01 11:56:49 1 24.800000Slippage: £402 vs expectations of £208
Ouch. I had a bad AUD and GBP trade which caused most of the problems here. Looking at the chart it looks as though the price I had when I submitted the order was stale. So it's bad data rather than a gap. Hopefully these should even out over the year so I don't have to go through the effort of manually cleaning them out of the database. Due to latency these figures can only ever be an estimate.
GAT
With this in mind, if someone had all of their capital in a trading system, would it be worth including 'buy and hold' as one of the strategies to add diversity, in addition to other strategies like trend following and carry?
Yes. I would put perhaps half my portfolio in a long only (or what I call the 'one rule' rule in my book) investment in "things that I expected to produce a positive return:" equities, bonds, short vol, perhaps some gold as an inflation hedge.
GAT
I've been reading and coding up the framework you describe in your book.
One question: when computing costs you use the volatility normalized turnover. If this is calculated in backtesting do we simply compute the number of instrument blocks traded at each specific date, divide this by the instrument volatility at that date, divide it by two so that it's a count of the number of round trips, and then sum the results over an entire year?
This annual volatility normalized turnover would then be multiplied by the standardized cost for the instrument and subtracted from the pre-cost SR. Is this the correct process?
I tried to follow this in the book but wasn't completely sure.
Thank you!
One question: when computing costs you use the volatility normalized turnover. If this is calculated in backtesting do we simply compute the number of instrument blocks traded at each specific date, divide this by the instrument volatility at that date, divide it by two so that it's a count of the number of round trips, and then sum the results over an entire year?
This annual volatility normalized turnover would then be multiplied by the standardized cost for the instrument and subtracted from the pre-cost SR. Is this the correct process?
I tried to follow this in the book but wasn't completely sure.
Thank you!
Hi
Turnover is basically trades / 2 x absolute average position
From page 186 of the print edition (chapter 12 'Estimating the number of round trips'):
Turnover=
Average number of blocks traded per year / (2 x average absolute number of blocks held)
So suppose your block is one futures contract and you trade daily. If your series of positions is p0, p1, ... pt
and you have N years of trading history then turnover=
(1/N)*(abs(p1-p0)+ans(p2-p1)+ ... ans(pt - pt-1)) / (2 x mean(abs(p0), abs(p1), abs(p2), ....)
I can also write this more conveniently as 250 (# of business days in a year) times the average daily turnover:
(250)*(mean(abs(p1-p0)+ans(p2-p1)+ ... ans(pt - pt-1)) / (2 x mean(abs(p0), abs(p1), abs(p2), ....)
Now for long backtests this might not be too accurate, especially if volatility has changed a lot over time. I can also rewrite the above as
(250)* ([abs(p1-p0)/ (2 x P0)] + [abs(p2-p1)/ (2 x P1)] + ....[abs(pt-pt-1)/ (2 x Pt-1)]
Where P is a smooth moving average of absolute position
(technically this is not exactly equal to the previous expression, but takes a moving average rather than one over the whole backtest)
Or I could also write:
(250)* ([abs(p1-p0)/ 2 x A0] + [abs(p2-p1)/ 2 x A1] + ....[abs(pt-pt-1)/ 2 x At-1]
Where A0... is the position I would get with a forecast of +10 given the vol etc at that time (the 'no rule' rule position).
[Recall that any position = instr. weight * IDM * forecast * daily cash vol target / ( 10 * price vol per day * block value * fx rate )
And for A0.... At-1 the forecast is +10]
This is probably the best estimator, . Notice that if we're measuring the turnover of a FORECAST, rather than a final position, this becomes: (for forecast f1.... ft)
(250)* ([abs(f1-f0)/ 2 x 10] + [abs(f2-f1)/ 2 x 10] + ....[abs(ft-ft-1)/ 2 x 10]
Hope that makes sense
GAT
From what you've written it seems like you're estimating trading costs using this approach, rather than just computing them directly. There are a few things I can't follow here:
- The trading costs are in terms of normalized turnover. I thought this turnover was volatility normalized, but from what you've written above the turnover seems to be normalized by the average absolute number of blocks held. Is the average absolute number of blocks held equal to the volatility? Is the standardized cost also normalized in terms of average absolute number of blocks held?
- Wouldn't it just be simpler to price in the cost of trading directly in the account currency, e.g. if 10 blocks of an instrument are bought on a particular day, and the cost per block is $8 (bid-ask spread + commission), then just subtract $80 from the account value at the end of the day in the back test. What advantage does using the normalized turnover and standardized cost have over this simple approach?
Last edited: