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updated and re-run examples/timing.py
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@enzbus
enzbus committed May 12, 2024
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124 changes: 64 additions & 60 deletions docs/_static/timing_output.txt
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Original file line number Diff line number Diff line change
@@ -1,97 +1,101 @@
Updating data.......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................


FIRST RUN; RESULT:
FIRST RUN
BACK-TEST TOOK: 260.66519236564636
SIMULATOR + POLICY TIMES: 260.6166265010834
AVERAGE TIME PER ITERATION: 0.20716742965109966
RESULT:

#################################################################
Universe size 502
Initial timestamp 2019-03-22 13:30:00+00:00
Final timestamp 2024-03-20 13:30:00+00:00
Number of periods 1258
Universe size 501
Initial timestamp 2019-05-13 13:30:00+00:00
Final timestamp 2024-05-10 13:30:00+00:00
Number of periods 1259
Initial value (USDOLLAR) 1.000e+06
Final value (USDOLLAR) 2.898e+06
Profit (USDOLLAR) 1.898e+06
Final value (USDOLLAR) 4.112e+06
Profit (USDOLLAR) 3.112e+06

Avg. return (annualized) 52.0%
Volatility (annualized) 77.3%
Avg. excess return (annualized) 50.0%
Avg. active return (annualized) 50.0%
Excess volatility (annualized) 77.3%
Active volatility (annualized) 77.3%
Avg. return (annualized) 59.4%
Volatility (annualized) 77.9%
Avg. excess return (annualized) 57.4%
Avg. active return (annualized) 57.4%
Excess volatility (annualized) 77.9%
Active volatility (annualized) 77.9%

Avg. growth rate (annualized) 21.3%
Avg. excess growth rate (annualized) 19.4%
Avg. active growth rate (annualized) 19.4%
Avg. growth rate (annualized) 28.3%
Avg. excess growth rate (annualized) 26.3%
Avg. active growth rate (annualized) 26.3%

Avg. StocksTransactionCost 0bp
Max. StocksTransactionCost 9bp
Avg. StocksHoldingCost 1bp
Max. StocksHoldingCost 5bp

Sharpe ratio 0.65
Information ratio 0.65
Sharpe ratio 0.74
Information ratio 0.74

Avg. drawdown -47.0%
Min. drawdown -89.1%
Avg. leverage 300.0%
Max. leverage 403.0%
Avg. turnover 3.8%
Avg. drawdown -48.2%
Min. drawdown -88.3%
Avg. leverage 299.9%
Max. leverage 399.6%
Avg. turnover 3.9%
Max. turnover 150.0%

Avg. policy time 0.232s
Avg. simulator time 0.094s
Of which: market data 0.016s
Total time 408.823s
Avg. policy time 0.150s
Avg. simulator time 0.057s
Of which: market data 0.018s
Of which: result 0.015s
Total time 260.617s
#################################################################

BACK-TEST TOOK: 409.0286121368408
SIMULATOR + POLICY TIMES: 408.8231477737427
AVERAGE TIME PER ITERATION: 0.32523718995524475


SECOND RUN; RESULT:
SECOND RUN
BACK-TEST TOOK: 149.64058017730713
SIMULATOR + POLICY TIMES: 149.59178948402405
AVERAGE TIME PER ITERATION: 0.11891239227664868
RESULT:

#################################################################
Universe size 502
Initial timestamp 2019-03-22 13:30:00+00:00
Final timestamp 2024-03-20 13:30:00+00:00
Number of periods 1258
Universe size 501
Initial timestamp 2019-05-13 13:30:00+00:00
Final timestamp 2024-05-10 13:30:00+00:00
Number of periods 1259
Initial value (USDOLLAR) 1.000e+06
Final value (USDOLLAR) 2.898e+06
Profit (USDOLLAR) 1.898e+06
Final value (USDOLLAR) 4.112e+06
Profit (USDOLLAR) 3.112e+06

Avg. return (annualized) 52.0%
Volatility (annualized) 77.3%
Avg. excess return (annualized) 50.0%
Avg. active return (annualized) 50.0%
Excess volatility (annualized) 77.3%
Active volatility (annualized) 77.3%
Avg. return (annualized) 59.4%
Volatility (annualized) 77.9%
Avg. excess return (annualized) 57.4%
Avg. active return (annualized) 57.4%
Excess volatility (annualized) 77.9%
Active volatility (annualized) 77.9%

Avg. growth rate (annualized) 21.3%
Avg. excess growth rate (annualized) 19.4%
Avg. active growth rate (annualized) 19.4%
Avg. growth rate (annualized) 28.3%
Avg. excess growth rate (annualized) 26.3%
Avg. active growth rate (annualized) 26.3%

Avg. StocksTransactionCost 0bp
Max. StocksTransactionCost 9bp
Avg. StocksHoldingCost 1bp
Max. StocksHoldingCost 5bp

Sharpe ratio 0.65
Information ratio 0.65
Sharpe ratio 0.74
Information ratio 0.74

Avg. drawdown -47.0%
Min. drawdown -89.1%
Avg. leverage 300.0%
Max. leverage 403.0%
Avg. turnover 3.8%
Avg. drawdown -48.2%
Min. drawdown -88.3%
Avg. leverage 299.9%
Max. leverage 399.6%
Avg. turnover 3.9%
Max. turnover 150.0%

Avg. policy time 0.135s
Avg. simulator time 0.079s
Of which: market data 0.014s
Total time 268.317s
Avg. policy time 0.066s
Avg. simulator time 0.053s
Of which: market data 0.017s
Of which: result 0.015s
Total time 149.592s
#################################################################

BACK-TEST TOOK: 268.54603695869446
SIMULATOR + POLICY TIMES: 268.3172538280487
AVERAGE TIME PER ITERATION: 0.2134584358218367
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23 changes: 16 additions & 7 deletions examples/timing.py
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Expand Up @@ -34,13 +34,16 @@
various components. You can expect it will do even more granular reporting in
future releases.
The results shown below are obtained on a Linux workstation with the latest
versions, at the time of writing, of most libraries.
.. note::
To reproduce what is shown here you should make sure that the first
time this script is run there are no covariance matrices already saved
for the historical market data used here. If you run it from scratch, that
is OK, but if you re-run this script it will pick up the covariance
matrices already estimated. There is currently (Cvxportfolio ``1.3.0``) no
matrices already estimated. There is currently (Cvxportfolio ``1.3``) no
easy way to remove caches other than manually deleting files in
``~/cvxportfolio_data``, which you can always safely do.
"""
Expand All @@ -67,7 +70,7 @@
# number we had in the paper examples
NUM_RISK_FACTORS = 15

# if you change this to 2 (quadratic model) the resulting problem is a QP
# if you change this to 2 (quadratic model) the resulting program is a QP
# and can be solved faster
TCOST_EXPONENT = 1.5

Expand All @@ -83,13 +86,19 @@
cvx.LeverageLimit(3),
],

# You can select any CVXPY solver here to see how it affects
# performance of your particular problem. This one is the default for
# this type of problems
solver='ECOS',
# You can select any CVXPY-interfaced solver here to see how it affects
# execution time of your particular program. Different solvers apply
# different roundings and other numerical heuristics; their solutions
# may also have (small) differences in other back-test statistics, such
# as Sharpe Ratio. This solver is the default open-source one for this
# type of programs, as of CVXPY 1.5.0. Other open-source solvers that
# work well for this type of programs are ECOS and SCS, and there are
# numerous commercial ones as well, see the CVXPY docs for a full list
# https://www.cvxpy.org/tutorial/solvers/index.html
solver='CLARABEL',

# this is a CVXPY compilation flag, it is recommended for large
# optimization problems (like this one) but not for small ones
# optimization programs (like this one) but not for small ones
ignore_dpp=True,

# you can add any other cvxpy.Problem.solve option
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