I wrote this simple modification to "enhancing mean reversion algorithms" a while ago. I added some common sense and made it long only. Found it while going through old algorithms and figured I might as well post it.
Pretty neat. How do you get it to stop borrowing cash ... ?
def maximize (context, data):
if context.Maximize == True:
currentHold = len(context.portfolio.positions)
for sec in context.portfolio.positions:
order_target_percent(sec, 1/currentHold) <-------------------- I changed this from 1.75 to 1 and it seemed to stop going into negative cash, but don't know if what I did was the best way to accomplish it.
record(leverage = context.account.leverage)
first id get rid of currentHold since its just = to len(context.portfolio.positions) (im sure i had a reason for it existing at some point.
if you want to borrow money, you order over 100% (1) 1.75 is the safe range where you can pretty much always order and never worry about the orders canceling due to margin requirements (for me at least)
btw you may want to change that 1 from an integer to a float value. idk if it matters here or not.
but yeah, that is the best way to accomplish ordering only 100% of your total account value.
def maximize (context, data):
if context.Maximize == True:
for sec in context.portfolio.positions:
order_target_percent( sec, 1.0/len(context.portfolio.positions) )
Started paper trading with this today and realized that it will most likely break the day trading rule on Robinhood ... Will have to adjust code for that
I dont think it day trades but it might. there are situations where it will sell off specific stocks (large losses). There is an issue that, somehow, even when you only order using positive values, quantopian opens a short position. I'm looking into it before I come out and say that they have a legitimate bug on their hands. so if the algorithm opens short positions, the next minute it will close that position. with robinhood, this isnt an issue since you cant open short positions.
So you would need to keep upgrading your Robinhood Gold as this progresses?
I put in the IB commissions and it didn't end well -99%
I was attempting to change the timing so it entered 6 days after the move instead of 1 day after the move.
Tyler: yes, I use the algorithm attached to this post with robinhood gold currently. This is one of two algorithms that control the same account.
Robinhood gold is set to 2000 and stays that way the whole way through.
Eric: What you changed is this: The algorithms looks to see if the price has changed in a certain way recently. your changed extends the idea of ignoring the past 6 days which has been shown to yield higher returns. I am running a 14 year backtest on this change right now. Will post results when they are in.
so you would have to stop the algorithm once you made enough to get more margin and then change the # and start is back up again?
Tyler: yes. pretty much. I keep a notebook with all the algorithms that have run to on that account that i use to figure out the total returns.
Eric: This is why I always do the longest backtest possible. your changes show promise, but they need improvement.
Is there any reason why this has been flat since February?
It has been on a tear since 2003, but for some reason this year has been mostly flat.
There are weird things happening in my paper trade - like shares disappearing. It will buy 20 shares of X stock, but only sell 15 - but the 5 remaining are not showing in the current holding. Anyone else seeing this with paper trading/live trading?
Can someone please re-post this algo with slippage enabled? Set it at either $0.01/share fixed slippage or the default volume share slippage model. Mine keeps crashing for some reasons... I also suspect that this strategy is not realistic because slippage and commissions are not baked into any of the shared algos above. Hopefully I am wrong.
2017-05-24 13:00 _pvr:287 INFO PvR 0.0285 %/day cagr 0.613 Portfolio value 9694961 PnL 9684961
2017-05-24 13:00 _pvr:288 INFO Profited 9684961 on 9368814 activated/transacted for PvR of 103.4%
2017-05-24 13:00 _pvr:289 INFO QRet 96849.61 PvR 103.37 CshLw -7892046 MxLv 3.58 MxRisk 9368814 MxShrt -9368814
2017-05-24 13:00 pvr:364 INFO 2003-01-02 to 2017-05-24 $10000 2017-08-15 21:55 US/Pacific
Runtime 1 hr 43.9 min
I only looked at the first algo, is there a version here that avoids 9.3 million short and 7.8 million margin? Returns on the amount invested are just 103%. Also consider track_orders.
@Kory - Slippage does need to be added, but not commissions as this is targeted to Robinhood - if you are taking margin you will need to manually factor the monthly cost
@Blue - I'll see if I can code in the track_orders. Also, Nicholas Forneris' algo on 6/20/2017 should be non margin
Here is a log of the original algorithm. Can you help review the log?:
It looks like it is selling shares it doesn't have - IE. Shorting.
2017-07-03 09:31 pvr:376 INFO 2017-07-03 to 2017-08-14 10000 minute
2017-07-03 12:03 pvr:471 INFO 153 MaxLv 0.84 QRet 0.0 PvR 0.0 CshLw 1597 Shrt 0
2017-07-03 12:11 pvr:471 INFO 161 MaxLv 1.66 QRet -0.1 PvR -0.0 CshLw -6570 Shrt 0
2017-07-05 12:11 _trac:271 INFO 161 Buy 12 EIX (3) at 76.70 cash -5845 5393
2017-07-05 12:13 _trac:271 INFO 163 Bot 12 EIX (15) at 76.69 cash -6765 5393
2017-07-05 12:13 pvr:471 INFO 163 MaxLv 1.68 QRet -0.3 PvR -0.2 CshLw -6765 Shrt 0
2017-07-06 12:01 _trac:271 INFO 151 Sell -8 CLX (8) at 133.62 e580
2017-07-06 12:02 _trac:271 INFO 152 Sold -8 CLX (-8) at 133.65 e580
2017-07-07 12:03 _trac:271 INFO 153 Buy 5 SJM _ at 115.47 e743
2017-07-07 12:04 _trac:271 INFO 154 Bot 5 SJM (5) at 115.45 e743
2017-07-07 12:11 pvr:471 INFO 161 MaxLv 1.72 QRet -0.6 PvR -0.4 CshLw -7132 Shrt 0
2017-07-10 12:02 _trac:271 INFO 152 Sell -16 VTR (16) at 67.49 bb64
2017-07-10 12:02 _trac:271 INFO 152 Sell -25 PEG (25) at 42.10 219a
2017-07-10 12:03 _trac:271 INFO 153 Sold -16 VTR _ at 67.49 bb64
2017-07-10 12:03 _trac:271 INFO 153 Sold -25 PEG _ at 42.09 219a
2017-07-11 12:01 _trac:271 INFO 151 Sell -12 DPS (12) at 89.04 cash -2744 2d2b
2017-07-11 12:02 _trac:271 INFO 152 Sold -12 DPS (-12) at 89.06 2d2b
2017-07-12 12:01 _trac:271 INFO 151 Sell -7 SJM (7) at 115.62 cash -4729 4f2e
2017-07-12 12:02 _trac:271 INFO 152 Sold -7 SJM (-7) at 115.58 4f2e
2017-07-12 12:11 _trac:271 INFO 161 Buy 7 OMC (5) at 80.98 cash -5719 4cbc
2017-07-12 12:11 _trac:271 INFO 161 Buy 5 HSY (4) at 104.69 cash -5719 e292
2017-07-12 12:12 _trac:271 INFO 162 Bot 7 OMC (12) at 80.96 cash -6809 4cbc
2017-07-12 12:12 _trac:271 INFO 162 Bot 5 HSY (9) at 104.72 cash -6809 e292
2017-07-13 12:02 _trac:271 INFO 152 Sell -9 HSY (9) at 104.81 19b1
2017-07-13 12:03 _trac:271 INFO 153 Sold -9 HSY _ at 104.80 19b1
2017-07-13 12:03 _trac:271 INFO 153 Buy 2 CLX _ at 131.48 5258
2017-07-13 12:03 _trac:271 INFO 153 Buy 1 AVB _ at 186.53 3943
2017-07-13 12:03 _trac:271 INFO 153 Buy 2 MON _ at 116.74 5b97
2017-07-13 12:04 _trac:271 INFO 154 Bot 2 CLX (2) at 131.49 5258
2017-07-13 12:04 _trac:271 INFO 154 Bot 1 AVB (1) at 186.54 3943
2017-07-13 12:04 _trac:271 INFO 154 Bot 2 MON (2) at 116.72 5b97
2017-07-13 12:11 _trac:271 INFO 161 Buy 3 CB (1) at 145.22 cash -6014 252f
2017-07-13 12:12 _trac:271 INFO 162 Bot 3 CB (4) at 145.24 cash -6449 252f
2017-07-14 12:02 _trac:271 INFO 152 Sell -17 HRL (17) at 32.86 d9b7
2017-07-14 12:02 _trac:271 INFO 152 Sell -4 CB (4) at 144.80 f63a
2017-07-14 12:03 _trac:271 INFO 153 Sold -17 HRL _ at 32.85 d9b7
2017-07-14 12:03 _trac:271 INFO 153 Sold -4 CB _ at 144.77 f63a
2017-07-14 12:03 _trac:271 INFO 153 Buy 11 PHM _ at 24.39 f6d2
2017-07-14 12:03 _trac:271 INFO 153 Buy 2 CME _ at 120.80 73a8
2017-07-14 12:04 _trac:271 INFO 154 Bot 11 PHM (11) at 24.39 f6d2
2017-07-14 12:04 _trac:271 INFO 154 Bot 2 CME (2) at 120.81 73a8
2017-07-17 12:03 _trac:271 INFO 153 Buy 4 LEN _ at 53.81 c53b
2017-07-17 12:04 _trac:271 INFO 154 Bot 4 LEN (4) at 53.79 c53b
2017-07-17 12:11 _trac:271 INFO 161 Buy 4 WOOF (2) at 92.45 cash -6054 cebe
2017-07-17 12:12 _trac:271 INFO 162 Bot 4 WOOF (6) at 92.44 cash -6424 cebe
2017-07-18 12:02 _trac:271 INFO 152 Sell -8 VLO (8) at 67.55 4efa
2017-07-18 12:03 _trac:271 INFO 153 Sold -8 VLO _ at 67.61 4efa
2017-07-18 12:11 _trac:271 INFO 161 Buy 5 IR (2) at 92.36 cash -6171 8a57
2017-07-18 12:12 _trac:271 INFO 162 Bot 5 IR (7) at 92.41 cash -6633 8a57
2017-07-19 12:03 _trac:271 INFO 153 Buy 1 MLM _ at 223.33 9095
2017-07-19 12:03 _trac:271 INFO 153 Buy 1 HUM _ at 237.31 2ea7
2017-07-19 12:04 _trac:271 INFO 154 Bot 1 HUM (1) at 237.33 2ea7
2017-07-19 12:05 _trac:271 INFO 155 Bot 1 MLM (1) at 223.13 9095
2017-07-20 12:01 _trac:271 INFO 151 Sell -5 TWX (5) at 99.46 cash -1504 0613
2017-07-20 12:01 _trac:271 INFO 151 Sell -2 ROP (2) at 234.57 cash -1504 4119
2017-07-20 12:02 _trac:271 INFO 152 Sold -5 TWX (-5) at 99.46 0613
2017-07-20 12:02 _trac:271 INFO 152 Sell -6 ABC (6) at 91.80 e8c1
2017-07-20 12:02 _trac:271 INFO 152 Sell -6 ALK (6) at 89.55 7529
2017-07-20 12:02 _trac:271 INFO 152 Sell -2 ROP (2) at 234.57 3360
2017-07-20 12:03 _trac:271 INFO 153 Sold -6 ALK _ at 89.57 7529
2017-07-20 12:03 _trac:271 INFO 153 Sold -2 ROP (-2) at 234.59 4119
2017-07-20 12:03 _trac:271 INFO 153 Sold -2 ROP (-2) at 234.59 3360
2017-07-20 12:03 _trac:271 INFO 153 Sold -6 ABC _ at 91.80 e8c1
2017-07-20 12:11 _trac:271 INFO 161 Buy 4 FRC (2) at 99.54 cash -6047 cf48
2017-07-20 12:13 _trac:271 INFO 163 Bot 4 FRC (6) at 99.56 cash -6445 cf48
2017-07-21 12:02 _trac:271 INFO 152 Sell -6 FRC (6) at 99.94 d81c
2017-07-21 12:04 _trac:271 INFO 154 Sold -6 FRC _ at 99.89 d81c
2017-07-21 12:11 _trac:271 INFO 161 Buy 2 FDX (1) at 211.39 cash -6271 6221
2017-07-21 12:13 _trac:271 INFO 163 Bot 2 FDX (3) at 211.43 cash -6694 6221
2017-07-25 12:03 _trac:271 INFO 153 Buy 1 SHW _ at 350.55 752f
2017-07-25 12:04 _trac:271 INFO 154 Bot 1 SHW (1) at 350.46 752f
2017-07-25 12:11 _trac:271 INFO 161 Buy 7 HCN (5) at 72.57 cash -5460 a320
2017-07-25 12:11 _trac:271 INFO 161 Buy 1 SHW (1) at 350.88 cash -5460 a6ff
2017-07-25 12:12 _trac:271 INFO 162 Bot 7 HCN (12) at 72.59 cash -6318 a320
2017-07-25 12:12 _trac:271 INFO 162 Bot 1 SHW (2) at 350.30 cash -6318 a6ff
2017-07-26 12:03 _trac:271 INFO 153 Buy 4 DLPH _ at 90.91 aea7
2017-07-26 12:04 _trac:271 INFO 154 Bot 4 DLPH (4) at 90.89 aea7
2017-07-26 12:11 _trac:271 INFO 161 Buy 11 VNO (5) at 78.02 cash -6266 1bc1
2017-07-26 12:12 _trac:271 INFO 162 Bot 11 VNO (16) at 78.04 cash -7124 1bc1
2017-07-27 12:11 _trac:271 INFO 161 Buy 5 CNC (4) at 82.27 cash -6123 859f
2017-07-27 12:12 _trac:271 INFO 162 Bot 5 CNC (9) at 82.24 cash -6534 859f
2017-07-28 12:11 _trac:271 INFO 161 Buy 5 UTX (2) at 118.80 cash -5779 467e
2017-07-28 12:11 _trac:271 INFO 161 Buy 5 KSU (3) at 102.80 cash -5779 9b41
2017-07-28 12:12 _trac:271 INFO 162 Bot 5 UTX (7) at 118.82 cash -6373 467e
2017-07-28 12:13 _trac:271 INFO 163 Bot 5 KSU (8) at 102.78 cash -6887 9b41
2017-07-31 12:02 _trac:271 INFO 152 Sell -5 MCK (5) at 161.88 e09c
2017-07-31 12:03 _trac:271 INFO 153 Sold -5 MCK _ at 162.02 e09c
2017-07-31 12:03 _trac:271 INFO 153 Buy 13 FNSR _ at 27.10 cff5
2017-07-31 12:03 _trac:271 INFO 153 Buy 4 EMN _ at 83.46 30aa
2017-07-31 12:03 _trac:271 INFO 153 Buy 2 VMC _ at 123.35 8fab
2017-07-31 12:04 _trac:271 INFO 154 Bot 13 FNSR (13) at 27.11 cff5
2017-07-31 12:04 _trac:271 INFO 154 Bot 2 VMC (2) at 123.34 8fab
2017-07-31 12:04 _trac:271 INFO 154 Bot 4 EMN (4) at 83.45 30aa
2017-07-31 12:11 _trac:271 INFO 161 Buy 7 OMC (4) at 78.54 cash -5695 0072
2017-07-31 12:11 _trac:271 INFO 161 Buy 3 TMO (2) at 175.78 cash -5695 efc5
2017-07-31 12:12 _trac:271 INFO 162 Bot 7 OMC (11) at 78.59 cash -6772 0072
2017-07-31 12:12 _trac:271 INFO 162 Bot 3 TMO (5) at 175.71 cash -6772 efc5
2017-08-01 12:11 _trac:271 INFO 161 Buy 7 JBHT (5) at 89.47 cash -6410 31ee
2017-08-01 12:12 _trac:271 INFO 162 Bot 7 JBHT (12) at 89.49 cash -7037 31ee
2017-08-02 12:11 _trac:271 INFO 161 Buy 3 PX (2) at 130.53 cash -5948 04d5
2017-08-02 12:11 _trac:271 INFO 161 Buy 5 ALK (3) at 85.16 cash -5948 1c31
2017-08-02 12:12 _trac:271 INFO 162 Bot 3 PX (5) at 130.55 cash -6765 04d5
2017-08-02 12:12 _trac:271 INFO 162 Bot 5 ALK (8) at 85.07 cash -6765 1c31
2017-08-03 12:01 _trac:271 INFO 151 Sell -5 PX (5) at 130.52 d3ab
2017-08-03 12:02 _trac:271 INFO 152 Sold -5 PX (-5) at 130.46 d3ab
2017-08-03 12:03 _trac:271 INFO 153 Buy 5 PX (-5) at 130.46 4b64
2017-08-03 12:04 _trac:271 INFO 154 Bot 5 PX (5) at 130.45 4b64
2017-08-03 12:11 _trac:271 INFO 161 Buy 9 QRVO (6) at 66.55 cash -6209 64e1
2017-08-03 12:12 _trac:271 INFO 162 Bot 9 QRVO (15) at 66.59 cash -6808 64e1
2017-08-04 12:11 _trac:271 INFO 161 Buy 9 LEA (6) at 144.86 cash -5962 aa2f
2017-08-04 12:12 _trac:271 INFO 162 Bot 9 LEA (15) at 144.87 cash -7266 aa2f
2017-08-04 12:12 pvr:471 INFO 162 MaxLv 1.72 QRet 3.0 PvR 1.7 CshLw -7266 Shrt 0
2017-08-07 12:03 _trac:271 INFO 153 Buy 3 UHS _ at 107.72 1527
2017-08-07 12:05 _trac:271 INFO 155 Bot 3 UHS (3) at 107.79 1527
2017-08-08 12:02 _trac:271 INFO 152 Sell -8 UHS (8) at 108.22 d8f0
2017-08-08 12:03 _trac:271 INFO 153 Sold -8 UHS _ at 108.20 d8f0
2017-08-09 12:11 _trac:271 INFO 161 Buy 3 AYI (2) at 194.22 cash -6286 2f12
2017-08-09 12:12 _trac:271 INFO 162 Bot 3 AYI (5) at 194.36 cash -6869 2f12
2017-08-10 12:02 _trac:271 INFO 152 Sell -5 AYI (5) at 189.90 9bd4
2017-08-10 12:04 _trac:271 INFO 154 Sold -5 AYI _ at 189.87 9bd4
2017-08-10 12:11 pvr:471 INFO 161 MaxLv 1.75 QRet 0.8 PvR 0.5 CshLw -7558 Shrt 0
2017-08-11 12:11 _trac:271 INFO 161 Buy 3 TIF (4) at 90.45 cash -6374 6d1f
2017-08-11 12:11 _trac:271 INFO 161 Buy 3 AAP (3) at 105.95 cash -6374 3aa3
2017-08-11 12:12 _trac:271 INFO 162 Bot 3 AAP (6) at 105.92 cash -6963 3aa3
2017-08-11 12:12 _trac:271 INFO 162 Bot 3 TIF (7) at 90.46 cash -6963 6d1f
2017-08-14 12:11 _trac:271 INFO 161 Buy 36 GRUB (12) at 54.37 cash -5641 31fe
2017-08-14 12:13 _trac:271 INFO 163 Bot 36 GRUB (48) at 54.34 cash -7597 31fe
2017-08-14 12:13 pvr:471 INFO 163 MaxLv 1.76 QRet 4.6 PvR 2.6 CshLw -7597 Shrt 0
2017-08-14 16:00 _pvr_:447 INFO PvR 0.0730 %/day 2017-07-03 to 2017-08-14 10000 minute
2017-08-14 16:00 _pvr_:450 INFO Profited 386 on 17597 activated/transacted for PvR of 2.2%
2017-08-14 16:00 _pvr_:453 INFO QRet 3.86 PvR 2.19 CshLw -7597 MxLv 1.76 RskHi 17597 Shrts 0
End of logs.
@Tyler, you should always bake both slippage and commissions into your backtest. Commissions can be $0.005/share with $1 minimum per order (IB's structure) and slippage can be default or just a fixed $0.01/share. I know RH doesn't charge commissions but this will help stress test the Algo and its robustness. If $0.01/share changes your results that much then no way it will survive IRL. And seeing how this algo trades, I can almost guarantee you it will not work IRL due to slippage, especially since it trades individual stocks and Robinhood can have larger bid/ask spreads on some stocks
I added slippage of 0.01/share, but not commissions as this is meant for Robinhood - DD is 100% in IB due to commissions.
@Blue - thanks for the message. I updated my post.
The backtest also doesn't match what was happening paper trading either. In live paper trading it sold all positions and went long SPXS - whereas in the backtest it did not. I would say this strategy is not valid.
Shouldn't be in negative cash immediately so it must be missing some orders. Try this, remove any calls to it and use this scheduling instead:
def initialize(context, data):
[your things]
for i in range(1, 391):
schedule_function(track_orders, date_rules.every_day(), time_rules.market_open(minutes=i))
To explain a bit:
Sell -6 ABC (6) at 91.80 e8c1
Sold -6 ABC _ at 91.80 e8c1
Current price that minute, and current number of shares in parens. Underscore means 0, no position (just easier to spot in a crowd than a 0).
You're right, some of those are negatives (short).
First column is minute of the day, 151 thru 153 here.
At the end, last four is the order id's. Sometimes those hexidecimals are just digits and no letters of course. In Notepad++ I highlight those which highlights matching instances.
2017-07-20 12:01 _trac:271 INFO 151 Sell -2 ROP (2) at 234.57 cash -1504 4119
2017-07-20 12:02 _trac:271 INFO 152 Sold -5 TWX (-5) at 99.46 0613
2017-07-20 12:02 _trac:271 INFO 152 Sell -6 ABC (6) at 91.80 e8c1
2017-07-20 12:02 _trac:271 INFO 152 Sell -6 ALK (6) at 89.55 7529
2017-07-20 12:02 _trac:271 INFO 152 Sell -2 ROP (2) at 234.57 3360
2017-07-20 12:03 _trac:271 INFO 153 Sold -6 ALK _ at 89.57 7529
2017-07-20 12:03 _trac:271 INFO 153 Sold -2 ROP (-2) at 234.59 4119
2017-07-20 12:03 _trac:271 INFO 153 Sold -2 ROP (-2) at 234.59 3360
2017-07-20 12:03 _trac:271 INFO 153 Sold -6 ABC _ at 91.80 e8c1
You have pvr there too and it didn't pick up shorting, something amiss, try the latest, I just updated it and scheduling that now too, no more calls from handle_data. I expected a look more like this, the latest, showing MxShrt:
2009-07-06 11:10 _pvr:89 INFO PvR 0.2127 %/day cagr 0.877 Portfolio value 1370135 PnL 370135
2009-07-06 11:10 _pvr:90 INFO Profited 370135 on 1381059 activated/transacted for PvR of 26.8%
2009-07-06 11:10 _pvr:91 INFO QRet 37.01 PvR 26.80 CshLw -15397 MxLv 1.07 MxRisk 1381059 MxShrt -1381059
It is just c.portfolio.positions[sid].amount revealing those negatives so there's definitely unwanted shorting it'll help you resolve. It was too easy in setting up track_orders to miss a spot, not a problem anymore with the switch to schedule_function, which surely will also make the source of that margin obvious since no orders would be overlooked that way. Unless Optimize API is present where orders are filled immediately, it could miss something then.
This has done well out of sample, anyone still trading it live and can verify results? Need to work some beta exposure into it though with some short positions.
@Chris D
I just tried running a backtest on the original algo posted and it hits -99% DD out of the gate....
Newbie question whats np.log (logarithm) used to calculate / indicate?
The script uses np.log(prices/prices.shift(1)) in the analyze function
@Jacob, interesting script.
Only changed a few numbers (13 in all). No change to the code itself.
The program could be improved. But, my first objective was to see if there was something there.
Instead of starting with $2 or $10k, I went directly for the $10 mil scenario and also opted to increase the trading activity.
The chart does say, for its 14-year simulation interval: 4.88 B in profits. Some might find it enough to compensate for the higher turbulence. It is a strategy that could be added to a group of strategies having lower volatility, thereby, reducing the overall turbulence.
However, I think there is something wrong with the above script. I don't have the tools to figure it out, but somebody else might.
Note that some leverage was applied: 1.25. Gross leverage came in at: 1.20. The presented outcome generated a lot more than enough to cover the leveraging fees.
The above chart is impressive.
@Jacob, after looking at some of the results of the presented screen snapshot above. I came to the conclusion that the strategy at that level is simply unfeasible.
It might work in theory, as the simulation can attest, but in real-life, the trades would not be executable as portrayed. It is all fine as the program starts, but as time progresses, the traded volume will become enormous as can be viewed in the following charts:
Also, these high volume trades are executed in a single minute. It is as if the 2.5% of available volume rule did not apply which in this case would have been quite a limiting factor.
Nonetheless, I suspect there are other problems in this script. My main question is: why did this program allow those results?
Changed a couple of numbers. It added some 2 B to the end game. But, then again. If it is not feasible, does it really matter?
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