From 7d5d45c0fbd619985c4d8c3d908bb1d30425071b Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Wed, 13 Dec 2023 11:35:07 +0400 Subject: [PATCH 01/16] run make fix on examples --- bumpversion.py | 13 +++++----- examples/__init__.py | 3 +-- examples/case_shiller.py | 24 +++++++++--------- examples/dow30.py | 30 +++++++++++------------ examples/etfs_example.py | 17 +++++++------ examples/hello_world.py | 7 +++--- examples/multiperiod_tcost.py | 5 ++-- examples/sp500_ndx100.py | 7 +++--- examples/timing.py | 21 ++++++++-------- examples/universes.py | 26 ++++++++++---------- examples/user_provided_forecasters.py | 35 +++++++++++++-------------- 11 files changed, 96 insertions(+), 92 deletions(-) diff --git a/bumpversion.py b/bumpversion.py index 0f9b3e4ab..23cec6dd8 100644 --- a/bumpversion.py +++ b/bumpversion.py @@ -14,22 +14,23 @@ """Find __version__ in __init__.py in file tree (BFS) and upgrade. Additionally, look for version string in any setup.py, pyproject.toml, -and conf.py and do the same. Version is in the format X.Y.Z, +and conf.py and do the same. Version is in the format X.Y.Z, where X, Y, and Z are integers. Take argument revision (Z -> Z+1), minor (Y -> Y+1, Z -> 0), or major (X -> X+1, Y -> 0, Z -> 0). -Add the modifications to git staging, commit with version number and +Add the modifications to git staging, commit with version number and editable message (opens git configured text editor), tag with version number, -push everything to origin.""" +push everything to origin. +""" -from ast import literal_eval import subprocess +from ast import literal_eval from pathlib import Path def findversion(root='.'): """Find version number. Skip [env, venv, .*]. - + We use the first __init__.py with a __version__ that we find. :param root: Root folder of the project. @@ -61,7 +62,7 @@ def findversion(root='.'): def replaceversion(new_version, version, root='.'): """Replace version number. Skip [env, venv, .*]. - + We replace in all __init__.py, conf.py, setup.py, and pyproject.toml """ diff --git a/examples/__init__.py b/examples/__init__.py index 3462e96bb..127402b87 100644 --- a/examples/__init__.py +++ b/examples/__init__.py @@ -11,5 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""This package contains examples of typical usage of Cvxportfolio. -""" +"""This package contains examples of typical usage of Cvxportfolio.""" diff --git a/examples/case_shiller.py b/examples/case_shiller.py index eeb7dd111..e1fb1e1c5 100644 --- a/examples/case_shiller.py +++ b/examples/case_shiller.py @@ -1,9 +1,10 @@ import os -import cvxportfolio as cvx +import matplotlib.pyplot as plt import numpy as np import pandas as pd -import matplotlib.pyplot as plt + +import cvxportfolio as cvx # We test the Multi-Period Optimization model on a real estate # portfolio for which the transaction cost are large with respect @@ -11,7 +12,7 @@ # We use the Case-Shiller indices as proxies for (residential) real # estate prices. -# These are monthly time serieses of the Case-Shiller index for +# These are monthly time serieses of the Case-Shiller index for # a selection of US metropolitan areas. You can find them on the # website of FRED https://fred.stlouisfed.org/ UNIVERSE = [ @@ -36,8 +37,8 @@ 'DNXRSA', # Denver 'BOXRSA', # Boston ] - -# we assume that the cost of transacting + +# we assume that the cost of transacting # residential real estate is about 5% LINEAR_TCOST = 0.05 @@ -45,7 +46,7 @@ universe = UNIVERSE, # we enabled the default data interface to download index # prices from FRED - datasource='Fred', + datasource='Fred', costs = [cvx.TransactionCost( a=LINEAR_TCOST, b=None, # since we don't have market volumes, we can't use the @@ -67,7 +68,7 @@ NUM_FACTORS = 5 kappa = 0.1 -# This is the multi-period planning horizon. We plan for 6 months +# This is the multi-period planning horizon. We plan for 6 months # in the future. HORIZON = 6 @@ -77,10 +78,10 @@ for gamma_risk in np.logspace(0, 3, 10): policies.append(cvx.MultiPeriodOptimization( cvx.ReturnsForecast() - gamma_risk * ( - cvx.FactorModelCovariance(num_factors=NUM_FACTORS) + cvx.FactorModelCovariance(num_factors=NUM_FACTORS) + kappa * cvx.RiskForecastError()) - - cvx.TransactionCost(a=LINEAR_TCOST, b=None), - [cvx.LongOnly(applies_to_cash=True)], + - cvx.TransactionCost(a=LINEAR_TCOST, b=None), + [cvx.LongOnly(applies_to_cash=True)], planning_horizon=HORIZON, ) ) @@ -102,7 +103,7 @@ 'r*-', label='Multi-period optimization frontier' ) -plt.scatter([result_uniform.excess_returns.std() * np.sqrt(12)], +plt.scatter([result_uniform.excess_returns.std() * np.sqrt(12)], [result_uniform.excess_returns.mean() * 12], label='Uniform (1/n) allocation' ) @@ -115,4 +116,3 @@ fig.savefig('case_shiller_frontier.png') else: plt.show() - diff --git a/examples/dow30.py b/examples/dow30.py index eed4cefbb..a66175b1e 100644 --- a/examples/dow30.py +++ b/examples/dow30.py @@ -1,6 +1,6 @@ """Example of long-only portfolio among DOW-30 components. -Monthly rebalance, backtest spans from the start of data of the +Monthly rebalance, backtest spans from the start of data of the components of the current index (in the 60s), the other stocks enter the backtest as times goes on. It ends today. @@ -9,9 +9,9 @@ and the transaction cost term multiplier. (We don't use the holding cost term because the portfolio is long-only.) -All result objects are collected, and then the one with +All result objects are collected, and then the one with largest Sharpe ratio, and the one with largest growth rate, -are shown. +are shown. Finally, we show the effect of using symbolic hyper-parameters, :class:`cvxportfolio.Gamma`, as multipliers of the risk and transaction @@ -21,12 +21,11 @@ You can run this with this command from the parent directory: .. code-block:: bash - - python -m examples.dow30 + python -m examples.dow30 """ -# Uncomment the logging lines to get online information +# Uncomment the logging lines to get online information # from the parallel backtest routines # import logging @@ -35,10 +34,11 @@ import os -import cvxportfolio as cvx import matplotlib.pyplot as plt import numpy as np +import cvxportfolio as cvx + from .universes import DOW30 sim = cvx.StockMarketSimulator(DOW30, trading_frequency='monthly') @@ -50,17 +50,17 @@ def make_policy(gamma_trade, gamma_risk): :type gamma_trade: float or int :param gamma_risk: Risk model multiplier. :type gamma_risk: float or int - :return: Multi-period optimization policy with given + :return: Multi-period optimization policy with given hyper-parameter values. :rtype: cvxportfolio.Policy """ - return cvx.MultiPeriodOptimization(cvx.ReturnsForecast() - - gamma_risk * cvx.FactorModelCovariance(num_factors=10) - - gamma_trade * cvx.StocksTransactionCost(), + return cvx.MultiPeriodOptimization(cvx.ReturnsForecast() + - gamma_risk * cvx.FactorModelCovariance(num_factors=10) + - gamma_trade * cvx.StocksTransactionCost(), [cvx.LongOnly(), cvx.LeverageLimit(1)], planning_horizon=6, solver='ECOS') -keys = [(gamma_trade, gamma_risk) for +keys = [(gamma_trade, gamma_risk) for gamma_trade in np.array(range(10))/10 for gamma_risk in [.5, 1, 2, 5, 10]] ress = sim.backtest_many([make_policy(*key) for key in keys]) @@ -103,9 +103,9 @@ def make_policy(gamma_trade, gamma_risk): plt.savefig('dow30_uniform.png') print('\n\nHYPER-PARAMETER OPTIMIZATION') -policy = cvx.MultiPeriodOptimization(cvx.ReturnsForecast() - - cvx.Gamma() * cvx.FactorModelCovariance(num_factors=10) - - cvx.Gamma() * cvx.StocksTransactionCost(), +policy = cvx.MultiPeriodOptimization(cvx.ReturnsForecast() + - cvx.Gamma() * cvx.FactorModelCovariance(num_factors=10) + - cvx.Gamma() * cvx.StocksTransactionCost(), [cvx.LongOnly(), cvx.LeverageLimit(1)], planning_horizon=6, solver='ECOS') sim.optimize_hyperparameters(policy, objective='profit') diff --git a/examples/etfs_example.py b/examples/etfs_example.py index c502f16c2..7068faab5 100644 --- a/examples/etfs_example.py +++ b/examples/etfs_example.py @@ -4,15 +4,16 @@ policies with a grid of values for the risk term multiplier and the transaction cost term multiplier. -All result objects are collected, and then the one with +All result objects are collected, and then the one with largest Sharpe ratio, and the one with largest growth rate, -are shown. +are shown. """ -import cvxportfolio as cvx import numpy as np -# Uncomment the logging lines to get online information +import cvxportfolio as cvx + +# Uncomment the logging lines to get online information # from the parallel backtest routines # import logging @@ -39,14 +40,14 @@ 'TIP', # TIPS 'DBC', # commodities ] - + sim = cvx.StockMarketSimulator(UNIVERSE, trading_frequency='monthly') def make_policy(gamma_trade, gamma_risk): - return cvx.MultiPeriodOptimization(cvx.ReturnsForecast() - - gamma_risk * cvx.FactorModelCovariance(num_factors=10) - - gamma_trade * cvx.StocksTransactionCost(), + return cvx.MultiPeriodOptimization(cvx.ReturnsForecast() + - gamma_risk * cvx.FactorModelCovariance(num_factors=10) + - gamma_trade * cvx.StocksTransactionCost(), [cvx.LongOnly(), cvx.LeverageLimit(1)], planning_horizon=6, solver='ECOS') diff --git a/examples/hello_world.py b/examples/hello_world.py index fd628983c..865066ab6 100644 --- a/examples/hello_world.py +++ b/examples/hello_world.py @@ -1,16 +1,17 @@ import os -import cvxportfolio as cvx import matplotlib.pyplot as plt +import cvxportfolio as cvx + # risk aversion parameter (Chapter 4.2) # chosen to match resulting volatility with the # uniform portfolio (for illustrative purpose) -gamma = 2.5 +gamma = 2.5 # covariance forecast error risk parameter (Chapter 4.3) # this can help regularize a noisy covariance estimate -kappa = 0.05 +kappa = 0.05 objective = cvx.ReturnsForecast() - gamma * ( cvx.FullCovariance() + kappa * cvx.RiskForecastError() diff --git a/examples/multiperiod_tcost.py b/examples/multiperiod_tcost.py index 4c3017413..59ebca4a1 100644 --- a/examples/multiperiod_tcost.py +++ b/examples/multiperiod_tcost.py @@ -1,8 +1,9 @@ -import cvxportfolio as cvx import matplotlib.pyplot as plt import numpy as np import pandas as pd +import cvxportfolio as cvx + universe = ["AMZN", "AAPL", "MSFT", "GOOGL", "TSLA", "GM", 'NKE', 'MCD', 'GE', 'CVX'] # initialize the portfolio with a signle long position in AMZN @@ -32,4 +33,4 @@ print(result) # plot value and weights of the portfolio in time -result.plot() +result.plot() diff --git a/examples/sp500_ndx100.py b/examples/sp500_ndx100.py index bd29c0dbb..428df21b9 100644 --- a/examples/sp500_ndx100.py +++ b/examples/sp500_ndx100.py @@ -1,13 +1,14 @@ -import cvxportfolio as cvx import matplotlib.pyplot as plt -from .universes import SP500, NDX100 +import cvxportfolio as cvx + +from .universes import NDX100, SP500 ## This is an example of a very large backtest, ~600 names and ~6000 days ## with multi period optimization. It shows that all parts of the system scale ## to such usecases. -objective = cvx.ReturnsForecast() -.05 * cvx.ReturnsForecastError() \ +objective = cvx.ReturnsForecast() - .05 * cvx.ReturnsForecastError() \ - 5 * (cvx.FactorModelCovariance(num_factors=50) + 0.1 * cvx.RiskForecastError()) \ - cvx.StocksTransactionCost(exponent=2) - cvx.StocksHoldingCost() diff --git a/examples/timing.py b/examples/timing.py index e69cdc990..de7315952 100644 --- a/examples/timing.py +++ b/examples/timing.py @@ -1,9 +1,10 @@ import time -import cvxportfolio as cvx import matplotlib.pyplot as plt import pandas as pd +import cvxportfolio as cvx + from .universes import SP500 # we test the typical time it takes @@ -11,7 +12,7 @@ # in the same way as we did in Figure # 7.8 of the book -# NOTE: the first time you run this, it will +# NOTE: the first time you run this, it will # compute and cache the risk model terms (for # each day). The second time you run you should # see faster runtime. @@ -20,7 +21,7 @@ # on the solver time, but small GAMMA_RISK = 1. GAMMA_TRADE = 1. -GAMMA_HOLD = 1. +GAMMA_HOLD = 1. # the solve time grows linearly # with this. 15 is the same number @@ -35,14 +36,14 @@ # you can add any constraint or objective # term to see how it affects execution time policy = cvx.SinglePeriodOptimization( - objective = cvx.ReturnsForecast() + objective = cvx.ReturnsForecast() - GAMMA_RISK * cvx.FactorModelCovariance(num_factors=NUM_RISK_FACTORS) - GAMMA_TRADE * cvx.StocksTransactionCost(exponent=TCOST_EXPONENT) - GAMMA_HOLD * cvx.StocksHoldingCost(), constraints = [ cvx.LeverageLimit(3), ], - + # You can select any CVXPY # solver here to see how it # affects performance of your @@ -50,12 +51,12 @@ # is the default for this type # of problems. solver='ECOS', - - # this is a CVXPY compilation flag, it is + + # this is a CVXPY compilation flag, it is # recommended for large optimization problems # (like this one) but not for small ones ignore_dpp=True, - + # you can add any other cvxpy.Problem.solve option # here, see https://www.cvxpy.org/tutorial/advanced/index.html ) @@ -73,10 +74,10 @@ print('BACKTEST TOOK:', time.time() - s) print( - 'SIMULATOR + POLICY TIMES:', + 'SIMULATOR + POLICY TIMES:', result.simulator_times.sum() + result.policy_times.sum()) print( - 'AVERAGE TIME PER ITERATION:', + 'AVERAGE TIME PER ITERATION:', result.simulator_times.mean() + result.policy_times.mean()) # plot diff --git a/examples/universes.py b/examples/universes.py index 88fe10890..729ffb465 100644 --- a/examples/universes.py +++ b/examples/universes.py @@ -13,8 +13,8 @@ # limitations under the License. """This module contains up-to-date universes of stock tickers. -If you run it attempts to download updated lists from the relevant -Wikipedia pages and it rewrites itself. Be careful when you run it +If you run it attempts to download updated lists from the relevant +Wikipedia pages and it rewrites itself. Be careful when you run it and check that the results make sense. We could also save each universe in a ``json`` file. @@ -101,18 +101,18 @@ universes = { 'sp500': { - 'page':"http://en.wikipedia.org/wiki/List_of_S%26P_500_companies", - 'table_number': 0, + 'page': "http://en.wikipedia.org/wiki/List_of_S%26P_500_companies", + 'table_number': 0, 'column_number': 0, }, - 'ndx100':{ - 'page':"https://en.wikipedia.org/wiki/Nasdaq-100", - 'table_number': -1, + 'ndx100': { + 'page': "https://en.wikipedia.org/wiki/Nasdaq-100", + 'table_number': -1, 'column_number': 1, }, - 'dow30':{ - 'page':"https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average", - 'table_number': 0, + 'dow30': { + 'page': "https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average", + 'table_number': 0, 'column_number': 1, } } @@ -121,12 +121,12 @@ def get_column_wikipedia_page(page, table_number, column_number): """Get a column as list of strings from a table on wikipedia. This is adapted from: - + https://pythonprogramming.net/sp500-company-price-data-python-programming-for-finance/ - + :param page: Wikipedia URL. :type page: str - :param table_number: Which table on the page. + :param table_number: Which table on the page. :type table_number: int :param column_number: Which column to extract. :type column_number: int diff --git a/examples/user_provided_forecasters.py b/examples/user_provided_forecasters.py index 72e7da6c0..09360331d 100644 --- a/examples/user_provided_forecasters.py +++ b/examples/user_provided_forecasters.py @@ -1,9 +1,8 @@ -""" -REQUIRES cvxportfolio >= 0.4.4 +"""REQUIRES cvxportfolio >= 0.4.4. This example shows how the user can provide custom-made -predictors for expected returns and covariances, -at each point in time of the backtest. These can be +predictors for expected returns and covariances, +at each point in time of the backtest. These can be used seamlessly inside a cvxportfolio backtest routine. """ @@ -13,16 +12,16 @@ # Here we define a class to forecast expected returns class WindowMeanReturn: """Expected return as mean of recent window of past returns.""" - + def __init__(self, window=20): self.window = window - + def values_in_time(self, past_returns, **kwargs): """This method computes the quantity of interest. - + It has many arguments, we only need to use past_returns - in this case. - + in this case. + NOTE: the last column of `past_returns` are the cash returns. You need to explicitely skip them otherwise the compiler will throw an error. @@ -33,23 +32,23 @@ def values_in_time(self, past_returns, **kwargs): # Here we define a class to forecast covariances class WindowCovariance: """Covariance computed on recent window of past returns.""" - + def __init__(self, window=20): self.window = window - + def values_in_time(self, past_returns, **kwargs): """This method computes the quantity of interest. - + It has many arguments, we only need to use past_returns - in this case. - + in this case. + NOTE: the last column of `past_returns` are the cash returns. You need to explicitely skip them otherwise the compiler will throw an error. """ return past_returns.iloc[-self.window:, :-1].cov() - - + + # define the hyperparameters WINDOWMU = 125 WINDOWSIGMA = 125 @@ -60,11 +59,11 @@ def values_in_time(self, past_returns, **kwargs): policy = cvx.SinglePeriodOptimization( objective = cvx.ReturnsForecast(WindowMeanReturn(WINDOWMU)) - GAMMA_RISK * cvx.FullCovariance(WindowCovariance(WINDOWSIGMA)) - - GAMMA_TRADE * cvx.StocksTransactionCost(), + - GAMMA_TRADE * cvx.StocksTransactionCost(), constraints = [cvx.LongOnly(), cvx.LeverageLimit(1)] ) -# define the simulator +# define the simulator simulator = cvx.StockMarketSimulator(['AAPL', 'GOOG', 'MSFT', 'AMZN']) # backtest From 0ebd673a4888138cbdd2738e1cbfa5176bba70fc Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Wed, 13 Dec 2023 11:57:20 +0400 Subject: [PATCH 02/16] make fix handles subfolders --- Makefile | 8 ++++---- examples/paper_examples/__init__.py | 18 ++++++++++++++++++ 2 files changed, 22 insertions(+), 4 deletions(-) create mode 100644 examples/paper_examples/__init__.py diff --git a/Makefile b/Makefile index 03c75a3c6..ebfcef73d 100644 --- a/Makefile +++ b/Makefile @@ -37,7 +37,7 @@ test: ## run tests w/ cov report # $(BINDIR)/bandit $(PROJECT)/*.py $(TESTS)/*.py lint: ## run linter - $(BINDIR)/pylint $(PROJECT) #$(EXTRA_SCRIPTS) $(EXAMPLES) + $(BINDIR)/pylint $(PROJECT) $(EXTRA_SCRIPTS) $(EXAMPLES) $(BINDIR)/diff-quality --violations=pylint --config-file pyproject.toml docs: ## build docs @@ -52,10 +52,10 @@ coverage: ## open html cov report fix: ## auto-fix code # selected among many code auto-fixers, tweaked in pyproject.toml - $(BINDIR)/autopep8 -i $(PROJECT)/*.py $(TESTS)/*.py $(EXAMPLES)/*.py $(EXTRA_SCRIPTS) - $(BINDIR)/isort $(PROJECT)/*.py $(TESTS)/*.py $(EXAMPLES)/*.py $(EXTRA_SCRIPTS) + $(BINDIR)/autopep8 -i -r $(PROJECT) $(EXAMPLES) $(EXTRA_SCRIPTS) + $(BINDIR)/isort $(PROJECT) $(EXAMPLES) $(EXTRA_SCRIPTS) # this is the best found for the purpose - $(BINDIR)/docformatter --in-place $(PROJECT)/*.py $(TESTS)/*.py $(EXAMPLES)/*.py $(EXTRA_SCRIPTS) + $(BINDIR)/docformatter -r --in-place $(PROJECT) $(EXAMPLES) $(EXTRA_SCRIPTS) release: update lint test ## update version, publish to pypi $(BINDIR)/python bumpversion.py diff --git a/examples/paper_examples/__init__.py b/examples/paper_examples/__init__.py new file mode 100644 index 000000000..6d864d0ae --- /dev/null +++ b/examples/paper_examples/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""This package contains the restored versions of the original code examples +developed for the 2017 paper. You can find the originals at: + +https://github.com/cvxgrp/cvxportfolio/tree/0.0.X/examples +""" From a25b67115d818317702fd410221639d4578a7d7b Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Wed, 13 Dec 2023 13:58:05 +0400 Subject: [PATCH 03/16] tried data import via git restore --- data/returns.csv.gz | Bin 0 -> 3107075 bytes data/volumes.csv.gz | Bin 0 -> 4966358 bytes examples/paper_examples/data_risk_model.py | 52 +++++++++++++++++++++ 3 files changed, 52 insertions(+) create mode 100644 data/returns.csv.gz create mode 100644 data/volumes.csv.gz create mode 100644 examples/paper_examples/data_risk_model.py diff --git a/data/returns.csv.gz b/data/returns.csv.gz new file mode 100644 index 0000000000000000000000000000000000000000..73fcc0e2a163b88a2afb844107fee1f4fa9a34fc GIT binary patch literal 3107075 zcmV)AK*YZviwFo%r4v~K|8iw?b#iWVE@N|c0Cc^{k{m~}uDS15=}3sp!xAHbBBp}` z0kU>?ao(WcU)tQ=^!qg;c|{7vL?Sc7!`)0xhktnYfBwJ!y?y!e<@Wl`&)e&_UvIB} 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zxeu)eOiU3*VEO&?tm#|-QFU%}0AkiNvBu(kbSxIVW=K*Z`n!~Ii7V7i)Sy!_wh=qqlKW-K|(G zQjB#47tcJ4ggY=~c|&USyI|M`K2PcaRBAt$8SY(B8J%I%pV%s2r;W%AL3LzVkOxgS zv}%G!tFwBjZ1er6XlVjlP@%Y2=WFG7@?rWC3lTBPSql%M2z}mqG^Fr#l9@MFoQ#7d zRo@kt8n`E<09P5+5-axd41iLqGLM?m7SM$=NbqWq+sz;GYfU-9t1mIj7R=Hx4`hi< z=ZwGnQWv!AM;2oe!IzVk;G8QgRfy41iUQU=i&#l|eCEI(N~IEb%?uW*Fgm2k%8_ls zQOX)d@m^%h;^%ie3pPaE`Hgworj9xq=8I2`{;053bM<)gE4^BdQktq?a|gY&OM14$ zP1MZ#&5gFS{#I@LX_>0e>tE0pj@r_e;QPhj>R@%o@jw3y009600|1IWG!r3vo&Xm( B?Vtbv literal 0 HcmV?d00001 diff --git a/examples/paper_examples/data_risk_model.py b/examples/paper_examples/data_risk_model.py new file mode 100644 index 000000000..0523c0a11 --- /dev/null +++ b/examples/paper_examples/data_risk_model.py @@ -0,0 +1,52 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""This is the restored version of the IPython notebook +used to download data for the original 2016 examples: + +https://github.com/cvxgrp/cvxportfolio/blob/0.0.X/examples/DataEstimatesRiskModel.ipynb + +The main reasons why the original can't be used any more: + +- the data source we used, "Quandl wiki" historical stock market data, + is defunct. Fortunately we saved the data in this repo, which is the + reason why it's quite big, so we can reproduce the original results. +- Pandas dropped its Panel data structure (after which it is named!); we + used it to store the risk models. We now use multi-indexed Dataframes. + +The data files that we use from the notebook above in 2017 are: + +- returns.csv.gz: Historical close-to-close total returns; note that today + :class:`cvxportfolio.DownloadedMarketData` instead computes + historical open-to-open total returns, matching the model + from the paper. +- volumes.csv.gz: historical market volumes in USD, computed as volumes + in number of shares times the adjusted close price. + +These are for a selection of stocks from the components of SP500 in 2016. +The data cleaning logic is shown in the notebook. We don't use the +following files from that notebook: + +- sigmas.csv.gz: now computed automatically by + :class:`cvxportfolio.TransactionCost`; note that intraday returns were + used in the notebook, while now we use open-to-open returns. This may + cause some noticeable difference in the market impact term of the + transaction cost estimates. In practice, the market impact term is either + negligible for small to medium investors, or needs to be tuned for the + given assets (using historical realized costs) in case of a large investor. +- prices.csv.gz: unused +- sigma_estimate.csv.gz: now computed automatically by + :class:`cvxportfolio.TransactionCost` +- volume_estimate.csv.gz: now computed automatically by + :class:`cvxportfolio.TransactionCost` +""" From b2d6eaeb498872c1bf5ec44556322883a47353ae Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Wed, 13 Dec 2023 14:01:00 +0400 Subject: [PATCH 04/16] moved datafiles into paper_examples --- {data => examples/paper_examples}/returns.csv.gz | Bin {data => examples/paper_examples}/volumes.csv.gz | Bin 2 files changed, 0 insertions(+), 0 deletions(-) rename {data => examples/paper_examples}/returns.csv.gz (100%) rename {data => examples/paper_examples}/volumes.csv.gz (100%) diff --git a/data/returns.csv.gz b/examples/paper_examples/returns.csv.gz similarity index 100% rename from data/returns.csv.gz rename to examples/paper_examples/returns.csv.gz diff --git a/data/volumes.csv.gz b/examples/paper_examples/volumes.csv.gz similarity index 100% rename from data/volumes.csv.gz rename to examples/paper_examples/volumes.csv.gz From b658ca5cf4bb10ff33a0669a68e63dcc8dd1add2 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Wed, 13 Dec 2023 19:00:03 +0400 Subject: [PATCH 05/16] risk model computation --- examples/paper_examples/data_risk_model.py | 123 ++++++++++++++++++++- 1 file changed, 117 insertions(+), 6 deletions(-) diff --git a/examples/paper_examples/data_risk_model.py b/examples/paper_examples/data_risk_model.py index 0523c0a11..40a2866da 100644 --- a/examples/paper_examples/data_risk_model.py +++ b/examples/paper_examples/data_risk_model.py @@ -26,27 +26,138 @@ The data files that we use from the notebook above in 2017 are: -- returns.csv.gz: Historical close-to-close total returns; note that today +- ``returns.csv.gz``: Historical close-to-close total returns; note that today :class:`cvxportfolio.DownloadedMarketData` instead computes historical open-to-open total returns, matching the model from the paper. -- volumes.csv.gz: historical market volumes in USD, computed as volumes +- ``volumes.csv.gz``: historical market volumes in USD, computed as volumes in number of shares times the adjusted close price. These are for a selection of stocks from the components of SP500 in 2016. The data cleaning logic is shown in the notebook. We don't use the following files from that notebook: -- sigmas.csv.gz: now computed automatically by +- ``sigmas.csv.gz``: now computed automatically by :class:`cvxportfolio.TransactionCost`; note that intraday returns were used in the notebook, while now we use open-to-open returns. This may cause some noticeable difference in the market impact term of the transaction cost estimates. In practice, the market impact term is either negligible for small to medium investors, or needs to be tuned for the given assets (using historical realized costs) in case of a large investor. -- prices.csv.gz: unused -- sigma_estimate.csv.gz: now computed automatically by +- ``prices.csv.gz``: unused +- ``sigma_estimate.csv.gz``: now computed automatically by :class:`cvxportfolio.TransactionCost` -- volume_estimate.csv.gz: now computed automatically by +- ``volume_estimate.csv.gz``: now computed automatically by :class:`cvxportfolio.TransactionCost` """ + +from pathlib import Path + +import numpy as np +import pandas as pd + +import cvxportfolio as cvx + +def paper_market_data(): + """Build market data server for the paper's examples. + + The returns dataframe already includes the cash returns column + (as its last), we point the ``cash_key`` argument to its name. + + We also use the ``min_history`` argument, which, howerver ,in this case is + not needed (since we start our back-tests after the default minimum + history of one year). + + :return: Market data for the paper. + :rtype: :class:`cvxportfolio.UserProvidedMarketData` + """ + returns = pd.read_csv( + Path(__file__).parent / 'returns.csv.gz', index_col=0, parse_dates=[0]) + volumes = pd.read_csv( + Path(__file__).parent / 'volumes.csv.gz', index_col=0, parse_dates=[0]) + print(returns) + return cvx.UserProvidedMarketData( + returns=returns, volumes=volumes, + cash_key='USDOLLAR', min_history=pd.Timedelta('0d')) + + +def paper_risk_model(): + """Build low-rank risk model for the paper's examples. + + This is mostly a copy-paste of the last cell of the original IPython + notebook. The differences are that we reshape the data into multi-indexed + dataframes and we explicitely skip the cash column. + + :return: Factor exposures, factor covariances, idyosincratic risks. + :rtype: pandas.DataFrame, pandas.DataFrame, pandas.DataFrame + """ + + k=15 + + start_t = "2012-01-01" + end_t = "2016-12-31" + + returns = pd.read_csv( + Path(__file__).parent / 'returns.csv.gz', + index_col=0, parse_dates=[0]).iloc[:, :-1] # skip cash column + + first_days_month = pd.date_range( + start=returns.index[next( + i for (i,el) in enumerate(returns.index >= start_t) if el)-1], + end=returns.index[-1], freq='MS') + + print('Computing risk model...') + + factor_exposures = pd.DataFrame( + index = pd.MultiIndex.from_product([first_days_month, range(k)]), + columns = returns.columns, + ) + + factor_sigma = pd.DataFrame( + index = pd.MultiIndex.from_product([first_days_month, range(k)]), + columns = range(k), + ) + + idyosincratic = pd.DataFrame( + index = first_days_month, columns = returns.columns) + + for day in first_days_month: + used_returns = returns.loc[ + (returns.index < day) & (returns.index >= day-pd.Timedelta( + "730 days"))] + second_moment = ( + used_returns.values.T @ used_returns.values + / used_returns.values.shape[0]) + eival, eivec = np.linalg.eigh(second_moment) + factor_sigma.loc[day] = np.diag(eival[-k:]) + factor_exposures.loc[day] = eivec[:,-k:].T + idyosincratic.loc[day] = np.diag( + eivec[:,:-k] @ np.diag(eival[:-k]) @ eivec[:,:-k].T) + + return factor_exposures, factor_sigma, idyosincratic + + + +if __name__ == '__main__': + + md = paper_market_data() + + print('Trading calendar:') + print(md.trading_calendar()) + + print('Returns:') + print(md.returns) + + print('Volumes:') + print(md.volumes) + + factor_exposures, factor_sigma, idyosincratic = paper_risk_model() + + print("Factor exposures:") + print(factor_exposures) + + print("Factor covariances:") + print(factor_sigma) + + print("Idyosincratic risks:") + print(idyosincratic) From 7b4300cb8471591632cbebcd6ea8777b76260417 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Fri, 15 Dec 2023 14:37:51 +0400 Subject: [PATCH 06/16] Merge from master #2 --- Makefile | 8 +++---- bumpversion.py | 30 +++++++++++++++++++++---- examples/{dow30_example.py => dow30.py} | 28 +---------------------- 3 files changed, 31 insertions(+), 35 deletions(-) rename examples/{dow30_example.py => dow30.py} (76%) diff --git a/Makefile b/Makefile index e81692a89..ebfcef73d 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,7 @@ BUILDDIR = build ENVDIR = env BINDIR = $(ENVDIR)/bin EXTRA_SCRIPTS = bumpversion.py -EXAMPLES = examples/*.py +EXAMPLES = examples ifeq ($(OS), Windows_NT) BINDIR=$(ENVDIR)/Scripts @@ -34,7 +34,7 @@ test: ## run tests w/ cov report $(BINDIR)/coverage xml $(BINDIR)/diff-cover coverage.xml --config-file pyproject.toml # disabled for now, we need to change pickle as default on-disk cache - # $(BINDIR)/bandit $(PROJECT)/*.py $(PROJECT)/tests/*.py + # $(BINDIR)/bandit $(PROJECT)/*.py $(TESTS)/*.py lint: ## run linter $(BINDIR)/pylint $(PROJECT) $(EXTRA_SCRIPTS) $(EXAMPLES) @@ -65,8 +65,8 @@ release: update lint test ## update version, publish to pypi $(BINDIR)/twine upload --skip-existing dist/* examples: ## run examples for docs - for example in hello_world case_shiller dow30_example; \ - do env CVXPORTFOLIO_SAVE_PLOTS=1 $(BINDIR)/python examples/"$$example".py > docs/_static/"$$example"_output.txt; \ + for example in hello_world case_shiller universes dow30; \ + do env CVXPORTFOLIO_SAVE_PLOTS=1 $(BINDIR)/python -m examples."$$example" > docs/_static/"$$example"_output.txt; \ done mv *.png docs/_static/ diff --git a/bumpversion.py b/bumpversion.py index c7686efac..23cec6dd8 100644 --- a/bumpversion.py +++ b/bumpversion.py @@ -1,10 +1,22 @@ -"""Look for __version__ in any __init__.py recursively and upgrade. +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Find __version__ in __init__.py in file tree (BFS) and upgrade. Additionally, look for version string in any setup.py, pyproject.toml, and conf.py and do the same. Version is in the format X.Y.Z, where X, Y, and Z are integers. Take argument revision (Z -> Z+1), minor (Y -> Y+1, Z -> 0), or major (X -> X+1, Y -> 0, Z -> 0). -Return the updated version string. Add the modifications to git staging, commit with version number and editable message (opens git configured text editor), tag with version number, @@ -20,23 +32,33 @@ def findversion(root='.'): """Find version number. Skip [env, venv, .*]. We use the first __init__.py with a __version__ that we find. + + :param root: Root folder of the project. + :type root: Pathlib.Path or str + + :raises ValueError: No version found. + + :returns: Found version string. + :rtype: str """ p = Path(root) for fname in p.iterdir(): if fname.name == '__init__.py': - with open(fname) as f: + with open(fname, encoding="utf-8") as f: lines = f.readlines() for line in lines: if '__version__' in line: - return eval(line.split('=')[1]) + return literal_eval(line.split('=')[1]) if fname.is_dir(): if not (fname.name in ['env', 'venv'] or fname.name[0] == '.'): result = findversion(fname) if result: return result + raise ValueError('Not found any!') + def replaceversion(new_version, version, root='.'): """Replace version number. Skip [env, venv, .*]. diff --git a/examples/dow30_example.py b/examples/dow30.py similarity index 76% rename from examples/dow30_example.py rename to examples/dow30.py index 3d347127b..a66175b1e 100644 --- a/examples/dow30_example.py +++ b/examples/dow30.py @@ -1,12 +1,7 @@ """Example of long-only portfolio among DOW-30 components. -<<<<<<< HEAD:examples/dow30.py Monthly rebalance, backtest spans from the start of data of the components of the current index (in the 60s), the other stocks -======= -Monthly rebalance, backtest spans from the start of data of the -components of the current index (in the 60s) and the other stocks ->>>>>>> master:examples/dow30_example.py enter the backtest as times goes on. It ends today. This uses an explicit loop to create Multi Period Optimization @@ -22,15 +17,12 @@ :class:`cvxportfolio.Gamma`, as multipliers of the risk and transaction cost terms. We can optimize on those explicitely, by finding the values that maximize some back-test metric (in this case, profit). -<<<<<<< HEAD:examples/dow30.py You can run this with this command from the parent directory: .. code-block:: bash python -m examples.dow30 -======= ->>>>>>> master:examples/dow30_example.py """ # Uncomment the logging lines to get online information @@ -45,22 +37,13 @@ import matplotlib.pyplot as plt import numpy as np -<<<<<<< HEAD:examples/dow30.py import cvxportfolio as cvx from .universes import DOW30 -======= ->>>>>>> master:examples/dow30_example.py -UNIVERSE = ['MMM', 'AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CVX', 'CSCO', 'KO', 'DIS', 'DOW', - 'GS', 'HD', 'HON', 'IBM','INTC', 'JNJ', - 'JPM','MCD', 'MRK', 'MSFT', 'NKE', 'PG', 'CRM', 'TRV', 'VZ', 'V', 'WBA', 'WMT'] - - -sim = cvx.StockMarketSimulator(UNIVERSE, trading_frequency='monthly') +sim = cvx.StockMarketSimulator(DOW30, trading_frequency='monthly') def make_policy(gamma_trade, gamma_risk): -<<<<<<< HEAD:examples/dow30.py """Build MPO policy given risk and trans. cost multipliers. :param gamma_trade: Transaction cost multiplier. @@ -79,15 +62,6 @@ def make_policy(gamma_trade, gamma_risk): keys = [(gamma_trade, gamma_risk) for gamma_trade in np.array(range(10))/10 for gamma_risk in [.5, 1, 2, 5, 10]] -======= - return cvx.MultiPeriodOptimization(cvx.ReturnsForecast() - - gamma_risk * cvx.FactorModelCovariance(num_factors=10) - - gamma_trade * cvx.StocksTransactionCost(), - [cvx.LongOnly(), cvx.LeverageLimit(1)], - planning_horizon=6, solver='ECOS') - -keys = [(gamma_trade, gamma_risk) for gamma_trade in np.array(range(10))/10 for gamma_risk in [.5, 1, 2, 5, 10]] ->>>>>>> master:examples/dow30_example.py ress = sim.backtest_many([make_policy(*key) for key in keys]) From d7b1315577a3a111ad7de5d5a50bc3965e58504c Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Fri, 15 Dec 2023 14:39:59 +0400 Subject: [PATCH 07/16] Merge from master #3 --- examples/sp500_ndx100.py | 7 ++++--- examples/timing.py | 2 -- pyproject.toml | 9 +++++++-- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/examples/sp500_ndx100.py b/examples/sp500_ndx100.py index 6d64f9c09..428df21b9 100644 --- a/examples/sp500_ndx100.py +++ b/examples/sp500_ndx100.py @@ -15,8 +15,9 @@ constraints = [cvx.LeverageLimit(3)] -policy = cvx.MultiPeriodOptimization(objective, constraints, planning_horizon=3, ignore_dpp=True) - +policy = cvx.MultiPeriodOptimization( + objective, constraints, planning_horizon=3, ignore_dpp=True) + universe = sorted(set(SP500 + NDX100)) simulator = cvx.StockMarketSimulator(universe) @@ -26,4 +27,4 @@ print(result) # plot value and weights of the portfolio in time -result.plot() +result.plot() diff --git a/examples/timing.py b/examples/timing.py index e6c1a6d7a..de7315952 100644 --- a/examples/timing.py +++ b/examples/timing.py @@ -1,7 +1,6 @@ import time import matplotlib.pyplot as plt -import time import pandas as pd import cvxportfolio as cvx @@ -86,4 +85,3 @@ result.simulator_times.plot(label='simulator times') plt.legend() plt.show() - diff --git a/pyproject.toml b/pyproject.toml index 6482f5953..3ed2b0b40 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,7 +53,12 @@ accept-no-yields-doc = false fail_under = 99 [tool.diff_cover] -# this will be superflous once we push the above to 100 +# this will be superflous once we push coverage to 100 +compare_branch = "origin/master" +fail_under = 99 + +[tool.diff_quality] +# this will be superflous once we push pylint score to 10 compare_branch = "origin/master" fail_under = 99 @@ -65,4 +70,4 @@ select = ["W291","W293","W391","E231","E225","E303"] # tweaked to remove whitespaces and other simple fixes wrap-summaries = 0 wrap-descriptions = 0 -tab-width = 4 \ No newline at end of file +tab-width = 4 From e1e82b2ecb07cae401477de0401b22af8719543f Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Sat, 16 Dec 2023 12:41:24 +0400 Subject: [PATCH 08/16] docs examples structure --- docs/examples.rst | 10 +++++----- docs/examples/case_shiller.rst | 4 ++-- docs/examples/dow30.rst | 6 +++--- docs/examples/etfs.rst | 8 ++++++++ docs/examples/paper_examples.rst | 7 +++++++ docs/examples/paper_examples/data_risk_model.rst | 10 ++++++++++ docs/examples/timing.rst | 8 ++++++++ docs/examples/universes.rst | 12 ++++++++++++ docs/examples/user_provided_forecasters.rst | 0 examples/{etfs_example.py => etfs.py} | 0 10 files changed, 55 insertions(+), 10 deletions(-) create mode 100644 docs/examples/etfs.rst create mode 100644 docs/examples/paper_examples.rst create mode 100644 docs/examples/paper_examples/data_risk_model.rst create mode 100644 docs/examples/timing.rst create mode 100644 docs/examples/universes.rst create mode 100644 docs/examples/user_provided_forecasters.rst rename examples/{etfs_example.py => etfs.py} (100%) diff --git a/docs/examples.rst b/docs/examples.rst index abfb5e2df..a87901e6e 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -4,13 +4,13 @@ Examples Many `example scripts are available in the code repository. `_ We show some of them, along with their results, in the following pages: -.. - :doc:`Hello World example ` -.. - :doc:`Case-Shiller example ` - - .. toctree:: :maxdepth: 2 hello_world examples/case_shiller - examples/dow30 \ No newline at end of file + examples/dow30 + examples/timing + examples/universes + examples/etfs + examples/paper_examples \ No newline at end of file diff --git a/docs/examples/case_shiller.rst b/docs/examples/case_shiller.rst index 737616a05..cf74b6c78 100644 --- a/docs/examples/case_shiller.rst +++ b/docs/examples/case_shiller.rst @@ -1,5 +1,5 @@ -Case-Shiller Example -==================== +Case-Shiller multi-period +========================= This is an example that shows that Cvxportfolio can work as well with different asset classes, in this case a real estate portfolio. We use diff --git a/docs/examples/dow30.rst b/docs/examples/dow30.rst index 9f39728b4..6b8ff5852 100644 --- a/docs/examples/dow30.rst +++ b/docs/examples/dow30.rst @@ -1,11 +1,11 @@ -DOW30 Example +DOW30 monthly ============= This example script is -`available in the repository `_. +`available in the repository `_. See the docstring below for its explanation. -.. literalinclude:: ../../examples/dow30_example.py +.. literalinclude:: ../../examples/dow30.py :language: python This is the output printed to screen when executing this script. You can see diff --git a/docs/examples/etfs.rst b/docs/examples/etfs.rst new file mode 100644 index 000000000..7e46a8aa2 --- /dev/null +++ b/docs/examples/etfs.rst @@ -0,0 +1,8 @@ +Wide asset classes ETFs +======================= + +This example script is +`available in the repository `_. + +.. literalinclude:: ../../examples/etfs.py + :language: python diff --git a/docs/examples/paper_examples.rst b/docs/examples/paper_examples.rst new file mode 100644 index 000000000..f5373ab48 --- /dev/null +++ b/docs/examples/paper_examples.rst @@ -0,0 +1,7 @@ +Original examples from the paper +================================ + +.. toctree:: + :maxdepth: 2 + + paper_examples/data_risk_model \ No newline at end of file diff --git a/docs/examples/paper_examples/data_risk_model.rst b/docs/examples/paper_examples/data_risk_model.rst new file mode 100644 index 000000000..1df3d6ea7 --- /dev/null +++ b/docs/examples/paper_examples/data_risk_model.rst @@ -0,0 +1,10 @@ +Data and risk model estimates +============================= + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +.. literalinclude:: ../../../examples/paper_examples/data_risk_model.py + :language: python \ No newline at end of file diff --git a/docs/examples/timing.rst b/docs/examples/timing.rst new file mode 100644 index 000000000..4395ed060 --- /dev/null +++ b/docs/examples/timing.rst @@ -0,0 +1,8 @@ +Timing of back-test +=================== + +This example script is +`available in the repository `_. + +.. literalinclude:: ../../examples/timing.py + :language: python diff --git a/docs/examples/universes.rst b/docs/examples/universes.rst new file mode 100644 index 000000000..4e09bd0c1 --- /dev/null +++ b/docs/examples/universes.rst @@ -0,0 +1,12 @@ +Stock universes +=============== + +This example module is +`available in the repository `_. +See the docstring below for its explanation. It's used by other examples +to get lists of stock tickers corresponding to various indexes. We run +it periodically to update the lists to the current constituents of each +index. + +.. literalinclude:: ../../examples/universes.py + :language: python diff --git a/docs/examples/user_provided_forecasters.rst b/docs/examples/user_provided_forecasters.rst new file mode 100644 index 000000000..e69de29bb diff --git a/examples/etfs_example.py b/examples/etfs.py similarity index 100% rename from examples/etfs_example.py rename to examples/etfs.py From 23db272e85eda34b757d8a22e740d63901067782 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Sat, 16 Dec 2023 13:21:46 +0400 Subject: [PATCH 09/16] structure --- docs/examples/case_shiller.rst | 4 ++- docs/examples/dow30.rst | 1 + docs/examples/etfs.rst | 1 + docs/examples/paper_examples.rst | 11 +++++++- .../paper_examples/data_risk_model.rst | 7 +++++- docs/examples/paper_examples/hello_world.rst | 15 +++++++++++ .../paper_examples/multi_period_opt.rst | 15 +++++++++++ .../paper_examples/portfolio_simulation.rst | 15 +++++++++++ docs/examples/paper_examples/rank_and_spo.rst | 15 +++++++++++ .../paper_examples/real_time_optimization.rst | 15 +++++++++++ .../paper_examples/single_period_opt.rst | 15 +++++++++++ .../single_period_opt_lin_tcost.rst | 15 +++++++++++ .../examples/paper_examples/solution_time.rst | 15 +++++++++++ docs/examples/timing.rst | 1 + docs/examples/user_provided_forecasters.rst | 10 ++++++++ examples/case_shiller.py | 13 ++++++++++ examples/dow30.py | 13 ++++++++++ examples/etfs.py | 13 ++++++++++ examples/hello_world.py | 13 ++++++++++ examples/multiperiod_tcost.py | 13 ++++++++++ examples/paper_examples/data_risk_model.py | 18 ++++++------- examples/paper_examples/hello_world.py | 16 ++++++++++++ examples/paper_examples/multi_period_opt.py | 16 ++++++++++++ .../paper_examples/portfolio_simulation.py | 16 ++++++++++++ examples/paper_examples/rank_and_spo.py | 16 ++++++++++++ .../paper_examples/real_time_optimization.py | 16 ++++++++++++ examples/paper_examples/single_period_opt.py | 16 ++++++++++++ .../single_period_opt_lin_tcost.py | 16 ++++++++++++ examples/paper_examples/solution_time.py | 16 ++++++++++++ examples/sp500_ndx100.py | 25 ++++++++++++++++--- examples/timing.py | 14 +++++++++++ examples/user_provided_forecasters.py | 13 ++++++++++ pyproject.toml | 2 +- 33 files changed, 403 insertions(+), 17 deletions(-) create mode 100644 docs/examples/paper_examples/hello_world.rst create mode 100644 docs/examples/paper_examples/multi_period_opt.rst create mode 100644 docs/examples/paper_examples/portfolio_simulation.rst create mode 100644 docs/examples/paper_examples/rank_and_spo.rst create mode 100644 docs/examples/paper_examples/real_time_optimization.rst create mode 100644 docs/examples/paper_examples/single_period_opt.rst create mode 100644 docs/examples/paper_examples/single_period_opt_lin_tcost.rst create mode 100644 docs/examples/paper_examples/solution_time.rst create mode 100644 examples/paper_examples/hello_world.py create mode 100644 examples/paper_examples/multi_period_opt.py create mode 100644 examples/paper_examples/portfolio_simulation.py create mode 100644 examples/paper_examples/rank_and_spo.py create mode 100644 examples/paper_examples/real_time_optimization.py create mode 100644 examples/paper_examples/single_period_opt.py create mode 100644 examples/paper_examples/single_period_opt_lin_tcost.py create mode 100644 examples/paper_examples/solution_time.py diff --git a/docs/examples/case_shiller.rst b/docs/examples/case_shiller.rst index cf74b6c78..3346352d8 100644 --- a/docs/examples/case_shiller.rst +++ b/docs/examples/case_shiller.rst @@ -9,10 +9,12 @@ are comparable to the annual return on the asset, and we show that multi-period optimization is useful to correctly balance transaction cost and expected risk-adjusted return. This example script is -`available in the repository `_. +`available in the repository +`_. .. literalinclude:: ../../examples/case_shiller.py :language: python + :lines: 14- This is the output printed to screen when executing this script. You can see many statistics of the back-tests. diff --git a/docs/examples/dow30.rst b/docs/examples/dow30.rst index 6b8ff5852..5c619f485 100644 --- a/docs/examples/dow30.rst +++ b/docs/examples/dow30.rst @@ -7,6 +7,7 @@ See the docstring below for its explanation. .. literalinclude:: ../../examples/dow30.py :language: python + :lines: 14- This is the output printed to screen when executing this script. You can see many statistics of the back-tests. diff --git a/docs/examples/etfs.rst b/docs/examples/etfs.rst index 7e46a8aa2..015e1d7f2 100644 --- a/docs/examples/etfs.rst +++ b/docs/examples/etfs.rst @@ -6,3 +6,4 @@ This example script is .. literalinclude:: ../../examples/etfs.py :language: python + :lines: 14- diff --git a/docs/examples/paper_examples.rst b/docs/examples/paper_examples.rst index f5373ab48..8fb9277e3 100644 --- a/docs/examples/paper_examples.rst +++ b/docs/examples/paper_examples.rst @@ -4,4 +4,13 @@ Original examples from the paper .. toctree:: :maxdepth: 2 - paper_examples/data_risk_model \ No newline at end of file + paper_examples/hello_world + paper_examples/data_risk_model + paper_examples/multi_period_opt + paper_examples/portfolio_simulation + paper_examples/rank_and_spo + paper_examples/real_time_optimization + paper_examples/single_period_opt + paper_examples/single_period_opt_lin_tcost + paper_examples/solution_time + \ No newline at end of file diff --git a/docs/examples/paper_examples/data_risk_model.rst b/docs/examples/paper_examples/data_risk_model.rst index 1df3d6ea7..08b727d5b 100644 --- a/docs/examples/paper_examples/data_risk_model.rst +++ b/docs/examples/paper_examples/data_risk_model.rst @@ -6,5 +6,10 @@ This example script is `_. See the docstring below for its explanation. +This is a translation of the `original IPython notebook +`_ +which doesn't run any more for reasons explained below. + .. literalinclude:: ../../../examples/paper_examples/data_risk_model.py - :language: python \ No newline at end of file + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/hello_world.rst b/docs/examples/paper_examples/hello_world.rst new file mode 100644 index 000000000..53cb143b6 --- /dev/null +++ b/docs/examples/paper_examples/hello_world.rst @@ -0,0 +1,15 @@ +Hello World (original) +====================== + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/hello_world.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/multi_period_opt.rst b/docs/examples/paper_examples/multi_period_opt.rst new file mode 100644 index 000000000..52a0d2fda --- /dev/null +++ b/docs/examples/paper_examples/multi_period_opt.rst @@ -0,0 +1,15 @@ +Multi-period Optimization +========================= + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/multi_period_opt.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/portfolio_simulation.rst b/docs/examples/paper_examples/portfolio_simulation.rst new file mode 100644 index 000000000..90d09a0d6 --- /dev/null +++ b/docs/examples/paper_examples/portfolio_simulation.rst @@ -0,0 +1,15 @@ +Portfolio simulation +===================== + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/portfolio_simulation.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/rank_and_spo.rst b/docs/examples/paper_examples/rank_and_spo.rst new file mode 100644 index 000000000..35f3c31ea --- /dev/null +++ b/docs/examples/paper_examples/rank_and_spo.rst @@ -0,0 +1,15 @@ +Ranking and SPO +====================================== + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/rank_and_spo.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/real_time_optimization.rst b/docs/examples/paper_examples/real_time_optimization.rst new file mode 100644 index 000000000..3949579c6 --- /dev/null +++ b/docs/examples/paper_examples/real_time_optimization.rst @@ -0,0 +1,15 @@ +Real-time optimization +====================== + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/real_time_optimization.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/single_period_opt.rst b/docs/examples/paper_examples/single_period_opt.rst new file mode 100644 index 000000000..8e73051f6 --- /dev/null +++ b/docs/examples/paper_examples/single_period_opt.rst @@ -0,0 +1,15 @@ +Single-period optimization +========================== + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/single_period_opt.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/single_period_opt_lin_tcost.rst b/docs/examples/paper_examples/single_period_opt_lin_tcost.rst new file mode 100644 index 000000000..a4eb4cfe1 --- /dev/null +++ b/docs/examples/paper_examples/single_period_opt_lin_tcost.rst @@ -0,0 +1,15 @@ +Single-period optimization with linear transaction cost +======================================================= + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/single_period_opt_lin_tcost.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/paper_examples/solution_time.rst b/docs/examples/paper_examples/solution_time.rst new file mode 100644 index 000000000..bda9f2b76 --- /dev/null +++ b/docs/examples/paper_examples/solution_time.rst @@ -0,0 +1,15 @@ +Solution time +============= + +This example script is +`available in the repository +`_. +See the docstring below for its explanation. + +This is a translation of the `original IPython notebook +`_ +using Cvxportfolio's stable API. + +.. literalinclude:: ../../../examples/paper_examples/solution_time.py + :language: python + :lines: 14- \ No newline at end of file diff --git a/docs/examples/timing.rst b/docs/examples/timing.rst index 4395ed060..222a25417 100644 --- a/docs/examples/timing.rst +++ b/docs/examples/timing.rst @@ -6,3 +6,4 @@ This example script is .. literalinclude:: ../../examples/timing.py :language: python + :lines: 14- diff --git a/docs/examples/user_provided_forecasters.rst b/docs/examples/user_provided_forecasters.rst index e69de29bb..70044ef92 100644 --- a/docs/examples/user_provided_forecasters.rst +++ b/docs/examples/user_provided_forecasters.rst @@ -0,0 +1,10 @@ +User-provided forecasters +========================= + +This example script is +`available in the repository +`_. + +.. literalinclude:: ../../examples/user_provided_forecasters.py + :language: python + :lines: 14- diff --git a/examples/case_shiller.py b/examples/case_shiller.py index e1fb1e1c5..09ad44b3c 100644 --- a/examples/case_shiller.py +++ b/examples/case_shiller.py @@ -1,3 +1,16 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import os import matplotlib.pyplot as plt diff --git a/examples/dow30.py b/examples/dow30.py index a66175b1e..f7f910e34 100644 --- a/examples/dow30.py +++ b/examples/dow30.py @@ -1,3 +1,16 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. """Example of long-only portfolio among DOW-30 components. Monthly rebalance, backtest spans from the start of data of the diff --git a/examples/etfs.py b/examples/etfs.py index 7068faab5..e0f1cc4e4 100644 --- a/examples/etfs.py +++ b/examples/etfs.py @@ -1,3 +1,16 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. """ETFs example covering the main asset classes. This uses an explicit loop to create Multi Period Optimization diff --git a/examples/hello_world.py b/examples/hello_world.py index 865066ab6..f1db9f0fa 100644 --- a/examples/hello_world.py +++ b/examples/hello_world.py @@ -1,3 +1,16 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import os import matplotlib.pyplot as plt diff --git a/examples/multiperiod_tcost.py b/examples/multiperiod_tcost.py index 59ebca4a1..e224140f5 100644 --- a/examples/multiperiod_tcost.py +++ b/examples/multiperiod_tcost.py @@ -1,3 +1,16 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import matplotlib.pyplot as plt import numpy as np import pandas as pd diff --git a/examples/paper_examples/data_risk_model.py b/examples/paper_examples/data_risk_model.py index 40a2866da..70a1bbfdd 100644 --- a/examples/paper_examples/data_risk_model.py +++ b/examples/paper_examples/data_risk_model.py @@ -58,6 +58,7 @@ import cvxportfolio as cvx + def paper_market_data(): """Build market data server for the paper's examples. @@ -65,7 +66,7 @@ def paper_market_data(): (as its last), we point the ``cash_key`` argument to its name. We also use the ``min_history`` argument, which, howerver ,in this case is - not needed (since we start our back-tests after the default minimum + not needed (since we start our back-tests after the default minimum history of one year). :return: Market data for the paper. @@ -89,21 +90,21 @@ def paper_risk_model(): dataframes and we explicitely skip the cash column. :return: Factor exposures, factor covariances, idyosincratic risks. - :rtype: pandas.DataFrame, pandas.DataFrame, pandas.DataFrame + :rtype: pandas.DataFrame, pandas.DataFrame, pandas.DataFrame """ - k=15 + k = 15 start_t = "2012-01-01" end_t = "2016-12-31" returns = pd.read_csv( - Path(__file__).parent / 'returns.csv.gz', + Path(__file__).parent / 'returns.csv.gz', index_col=0, parse_dates=[0]).iloc[:, :-1] # skip cash column first_days_month = pd.date_range( start=returns.index[next( - i for (i,el) in enumerate(returns.index >= start_t) if el)-1], + i for (i, el) in enumerate(returns.index >= start_t) if el)-1], end=returns.index[-1], freq='MS') print('Computing risk model...') @@ -120,7 +121,7 @@ def paper_risk_model(): idyosincratic = pd.DataFrame( index = first_days_month, columns = returns.columns) - + for day in first_days_month: used_returns = returns.loc[ (returns.index < day) & (returns.index >= day-pd.Timedelta( @@ -130,14 +131,13 @@ def paper_risk_model(): / used_returns.values.shape[0]) eival, eivec = np.linalg.eigh(second_moment) factor_sigma.loc[day] = np.diag(eival[-k:]) - factor_exposures.loc[day] = eivec[:,-k:].T + factor_exposures.loc[day] = eivec[:, -k:].T idyosincratic.loc[day] = np.diag( - eivec[:,:-k] @ np.diag(eival[:-k]) @ eivec[:,:-k].T) + eivec[:, :-k] @ np.diag(eival[:-k]) @ eivec[:, :-k].T) return factor_exposures, factor_sigma, idyosincratic - if __name__ == '__main__': md = paper_market_data() diff --git a/examples/paper_examples/hello_world.py b/examples/paper_examples/hello_world.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/hello_world.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/multi_period_opt.py b/examples/paper_examples/multi_period_opt.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/multi_period_opt.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/portfolio_simulation.py b/examples/paper_examples/portfolio_simulation.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/portfolio_simulation.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/rank_and_spo.py b/examples/paper_examples/rank_and_spo.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/rank_and_spo.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/real_time_optimization.py b/examples/paper_examples/real_time_optimization.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/real_time_optimization.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/single_period_opt.py b/examples/paper_examples/single_period_opt.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/single_period_opt.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/single_period_opt_lin_tcost.py b/examples/paper_examples/single_period_opt_lin_tcost.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/single_period_opt_lin_tcost.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/solution_time.py b/examples/paper_examples/solution_time.py new file mode 100644 index 000000000..7e7c25a12 --- /dev/null +++ b/examples/paper_examples/solution_time.py @@ -0,0 +1,16 @@ +# Copyright 2016 Enzo Busseti, Stephen Boyd, Steven Diamond, BlackRock Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" + +import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/sp500_ndx100.py b/examples/sp500_ndx100.py index 428df21b9..675fc2479 100644 --- a/examples/sp500_ndx100.py +++ b/examples/sp500_ndx100.py @@ -1,13 +1,30 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""This is an example of a very large backtest. + +It has about ~600 names and ~6000 days, and uses multi period optimization. +It shows that all parts of the system scale to such usecases. Running it +might take a while (about half an hour on a 2020s computer), +especially the first run which computes and caches the risk model each day. +""" + import matplotlib.pyplot as plt import cvxportfolio as cvx from .universes import NDX100, SP500 -## This is an example of a very large backtest, ~600 names and ~6000 days -## with multi period optimization. It shows that all parts of the system scale -## to such usecases. - objective = cvx.ReturnsForecast() - .05 * cvx.ReturnsForecastError() \ - 5 * (cvx.FactorModelCovariance(num_factors=50) + 0.1 * cvx.RiskForecastError()) \ diff --git a/examples/timing.py b/examples/timing.py index de7315952..2a104fc30 100644 --- a/examples/timing.py +++ b/examples/timing.py @@ -1,3 +1,17 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""""" import time import matplotlib.pyplot as plt diff --git a/examples/user_provided_forecasters.py b/examples/user_provided_forecasters.py index 09360331d..975705a3e 100644 --- a/examples/user_provided_forecasters.py +++ b/examples/user_provided_forecasters.py @@ -1,3 +1,16 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. """REQUIRES cvxportfolio >= 0.4.4. This example shows how the user can provide custom-made diff --git a/pyproject.toml b/pyproject.toml index 3ed2b0b40..88fdaca7e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ dependencies = ["pandas", "numpy", "matplotlib", "requests", "cvxpy", [project.optional-dependencies] docs = ["sphinx", "furo"] dev = ["build", "twine", "coverage", "diff_cover", "pylint", "isort", - "autopep8", "docformatter"] + "autopep8", "docformatter", "beautifulsoup4"] [project.urls] Homepage = "https://www.cvxportfolio.com" From bb62e3c7603aed871285da6fd05620dc5bc3d454 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Sat, 16 Dec 2023 14:05:52 +0400 Subject: [PATCH 10/16] hello world translated and working --- examples/paper_examples/hello_world.py | 81 +++++++++++++++++++++++++- 1 file changed, 79 insertions(+), 2 deletions(-) diff --git a/examples/paper_examples/hello_world.py b/examples/paper_examples/hello_world.py index 7e7c25a12..593c783b6 100644 --- a/examples/paper_examples/hello_world.py +++ b/examples/paper_examples/hello_world.py @@ -11,6 +11,83 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""This is a simple example of back-tests with Cvxportfolio. +""" -import cvxportfolio as cvx \ No newline at end of file +import cvxportfolio as cvx +import pandas as pd +import matplotlib.pyplot as plt + + +# Download market data. +market_data = cvx.DownloadedMarketData( + universe = ['AMZN', 'GOOGL', 'TSLA', 'NKE'], + cash_key = 'USDOLLAR') + +print('Historical open-to-open total returns:') +print(market_data.returns) + +# Define transaction and holding cost models. +HALF_SPREAD = 10E-4 +BORROW_FEE = 1E-4 * (252 / 100) # in annualized percentage +tcost_model = cvx.TcostModel(a=HALF_SPREAD) +hcost_model = cvx.HcostModel(short_fees=BORROW_FEE) + +# As returns forecast, we simply take the historical means +# computed at each point in the back-test (looking only +# at past returns). That's the default behavior; we +# may as well pass a dataframe here with different predictions. +r_hat = cvx.ReturnsForecast() + +# As risk model, we choose the full historical covariance. +# It is computed every day using the full past historical +# returns at that point. (We may as well provide it as a +# dataframe.) +risk_model = cvx.FullSigma() + +# Constraint. +leverage_limit = cvx.LeverageLimit(3) + +# Define a single-period optimization policy; its +# objective function is maximized. +gamma_risk, gamma_trade, gamma_hold = 5., 1., 1. +spo_policy = cvx.SinglePeriodOpt( + objective = r_hat + - gamma_risk * risk_model + - gamma_trade * tcost_model + - gamma_hold * hcost_model, + constraints=[leverage_limit]) + +# Define the market simulator. +market_sim = cvx.MarketSimulator( + market_data = market_data, + costs = [ + cvx.TcostModel(a=HALF_SPREAD), + cvx.HcostModel(short_fees=BORROW_FEE)]) + +# Initial portfolio, uniform on non-cash assets. +init_portfolio = pd.Series( + index=market_data.returns.columns, data=250000.) +init_portfolio.USDOLLAR = 0 + +# Run two back-tests. +results = market_sim.run_multiple_backtest( + h=[init_portfolio]*2, + start_time='2013-01-03', end_time='2016-12-31', + policies=[spo_policy, cvx.Hold()]) + +print('Back-test result, single-period optimization policy:') +print(results[0]) + +print('Back-test result, Hold policy:') +print(results[1]) + +results[0].v.plot(label='SPO') +results[1].v.plot(label='Hold policy') +plt.title('Portfolio total value in time (USD)') +plt.legend() +plt.show() + +results[0].w.plot() +plt.title('SPO weights in time') +plt.show() From c5c0670a53700b339db80955b3f06fd33cd3e199 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Sun, 17 Dec 2023 19:26:00 +0400 Subject: [PATCH 11/16] risk models example --- TODOs_ROADMAP.rst | 19 ++++- examples/risk_models.py | 156 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 173 insertions(+), 2 deletions(-) create mode 100644 examples/risk_models.py diff --git a/TODOs_ROADMAP.rst b/TODOs_ROADMAP.rst index f6c30362a..3c853a277 100644 --- a/TODOs_ROADMAP.rst +++ b/TODOs_ROADMAP.rst @@ -65,14 +65,27 @@ their planned release. ``cvxportfolio.risks`` ---------------------- +``cvxportfolio.hyperparameters`` +------------------------- + +- [ ] Clean up interface w/ ``MarketSimulator``, right now it calls private + methods, maybe enough to make them public. Target ``1.0.4``. +- [ ] Add risk/fine default ``GammaTrade``, ``GammaRisk`` (which are + ``RangeHyperParameter``) modeled after original examples from paper. + Target ``1.1.0``. + ``cvxportfolio.policies`` ------------------------- +- [ ] Add `AllIn` policy, which allocates all to a single name (like + ``AllCash``). Target ``1.1.0``. + Optimization policies ~~~~~~~~~~~~~~~~~~~~~ - [ ] Improve behavior for infeasibility/unboundedness/solver error. Target ``1.1.0``. +- [ ] Improve ``__repr__`` method, now hard to read. Target ``1.0.4``. ``cvxportfolio.constraints`` ---------------------------- @@ -99,8 +112,10 @@ Development & testing Documentation ------------- -- Manual -- Quickstart +- [ ] Improve examples section, also how "Hello world" is mentioned in readme. + Target ``1.0.4``, PR #118. +- [ ] Manual. +- [ ] Quickstart, probably to merge into manual. Examples -------- diff --git a/examples/risk_models.py b/examples/risk_models.py new file mode 100644 index 000000000..2fd255af9 --- /dev/null +++ b/examples/risk_models.py @@ -0,0 +1,156 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test different choices of risk models, which has best performance? + +**WORK IN PROGRESS** + +On the Nasdaq 100 index, daily trading from 2016 to today: +- diagonal risk model +- diagonal risk model with risk forecast error +- full covariance +- full covariance with risk forecast error +- ... + +We test on a long-only portfolio and use automatic hyper-parameter +optimization to maximize the information ratio, in back-test, +versus an index ETF. +""" + +import os +from pprint import pprint + +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np + +import cvxportfolio as cvx +from .universes import DOW30 as UNIVERSE +#from .universes import NDX100 as UNIVERSE + + +# Index +INDEX = 'DIA' # +#INDEX = 'QQQ' + +# Times. +START = '2000-01-01' #'2016-01-01' +END = None #'2017-01-01' + +# Leverage. +LEVERAGE_LIMIT = 1. + +# Stock market simulator with default transaction and +# holding cost models. +simulator = cvx.StockMarketSimulator(UNIVERSE + [INDEX]) + +# Build benchmark. +all_in_index = pd.Series( + 0., index = simulator.market_data.returns.columns) +all_in_index[INDEX] = 1. +benchmark = cvx.FixedWeights(all_in_index) + +# Define hyper-parameter objects: +class GammaTradeCoarse(cvx.RangeHyperParameter): + """Transaction cost multiplier, coarse value range.""" + def __init__(self): + super().__init__( + values_range=np.arange(1,11), + current_value=1.) + +class GammaTradeFine(cvx.RangeHyperParameter): + """Transaction cost multiplier, fine value range.""" + def __init__(self): + super().__init__( + values_range=np.linspace(-1.,1.,51), + current_value=0) + +class GammaRiskCoarse(cvx.RangeHyperParameter): + """Risk term multiplier, coarse value range.""" + def __init__(self): + super().__init__( + values_range=np.arange(1,21), + current_value=1.) + +class GammaRiskFine(cvx.RangeHyperParameter): + """Risk term multiplier, fine value range.""" + def __init__(self): + super().__init__( + values_range=np.linspace(-1.,1.,51), + current_value=0) + +class Kappa(cvx.RangeHyperParameter): + """Risk forecast error multiplier, fine value range.""" + def __init__(self): + super().__init__( + values_range=np.linspace(0.,0.5), + current_value=0) + + +# We test these risk models, with symbolic hyper-parameters. +base_risk_models = [cvx.DiagonalCovariance(), cvx.FullCovariance()] +base_risk_models += [ + cvx.FactorModelCovariance(num_factors=num_factors) + for num_factors in [1, 2, 5, 10] +] + +# with hyper-parameters +RISK_MODELS = [ + (GammaRiskCoarse() + GammaRiskFine()) * base_risk_model + for base_risk_model in base_risk_models +] + +# with risk forecast error +RISK_MODELS += [ + (GammaRiskCoarse() + GammaRiskFine()) * ( + base_risk_model + Kappa() * cvx.RiskForecastError()) + for base_risk_model in base_risk_models +] + +results = {} + +for risk_model in RISK_MODELS: + print('Testing risk model:') + print(risk_model) + + results[repr(risk_model)] = {} + current_result = results[repr(risk_model)] + + # Build policy. + policy = cvx.SinglePeriodOpt( + objective = cvx.ReturnsForecast() + - (GammaTradeCoarse() + GammaTradeFine() + ) * cvx.StocksTransactionCost() + - risk_model, + constraints=[cvx.LongOnly(), cvx.LeverageLimit(LEVERAGE_LIMIT)], + benchmark=benchmark, + solver='CLARABEL') + + # Optimize HPs. + simulator.optimize_hyperparameters( + policy, start_time=START, end_time=END, + objective='information_ratio') + + print('Policy with optimized hyper-parameters:') + print(policy) + current_result['Optimized policy'] = repr(policy) + + # Run back-test with optimized HPs. + optimized_result = simulator.backtest( + policy, start_time=START, end_time=END) + + print('Back-test result with optimized hyper-parameters:') + print(optimized_result) + current_result['Optimized result'] = optimized_result + +pprint(results) From 1f7b2eff3807812124a142c79d1c52299c02d8a5 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Thu, 21 Dec 2023 09:21:10 +0400 Subject: [PATCH 12/16] managed to reproduce pre-2023 hello world exactly --- examples/paper_examples/hello_world.py | 117 ++++++++++++++++++++----- examples/risk_models.py | 38 ++++---- pyproject.toml | 1 + 3 files changed, 115 insertions(+), 41 deletions(-) diff --git a/examples/paper_examples/hello_world.py b/examples/paper_examples/hello_world.py index 593c783b6..6be6d3f55 100644 --- a/examples/paper_examples/hello_world.py +++ b/examples/paper_examples/hello_world.py @@ -12,38 +12,106 @@ # See the License for the specific language governing permissions and # limitations under the License. """This is a simple example of back-tests with Cvxportfolio. + +This is a close translation of what was done in `this notebook +`_. +In fact, you can see that the results are identical. + +The approach used here is not recommended; in particular we download +data externally (it is done better now by the automatic data download +and cleaning code we include in Cvxportfolio). The returns used here +are close-to-close total returns, while our interface computes correctly +the open-to-open total returns. + +In this example returns and covariances are forecasted externally, +while today this can be done automatically using the default forecasters +used by :class:`cvxportfolio.ReturnsForecast` and +:class:`cvxportfolio.FullCovariance`. + +Nevertheless, you can see by running this that we are still able to +reproduce exactly the behavior of the early development versions +of the library. + +.. note :: + + To run this, you need to install ``yfinance`` and + ``pandas_datareader``. + """ -import cvxportfolio as cvx -import pandas as pd import matplotlib.pyplot as plt +import pandas as pd + +import yfinance +import pandas_datareader as pdr + +import cvxportfolio as cvx + +# Download market data +tickers = ['AMZN', 'GOOGL', 'TSLA', 'NKE'] + +returns = pd.DataFrame(dict([(ticker, + yfinance.download(ticker)['Adj Close'].pct_change()) + for ticker in tickers])) + +returns["USDOLLAR"]=pdr.get_data_fred( + 'DFF', start="1900-01-01", + end=pd.Timestamp.today())['DFF']/(252*100) +returns = returns.fillna(method='ffill').iloc[1:] -# Download market data. -market_data = cvx.DownloadedMarketData( - universe = ['AMZN', 'GOOGL', 'TSLA', 'NKE'], +print('Returns') +print(returns) + +# Create market data server. +market_data = cvx.UserProvidedMarketData( + returns = returns, cash_key = 'USDOLLAR') -print('Historical open-to-open total returns:') + +# Today we'd do all the above by (no external packages needed): +# market_data = cvx.DownloadedMarketData( +# universe = ['AMZN', 'GOOGL', 'TSLA', 'NKE'], +# cash_key = 'USDOLLAR') + + +print('Historical returns:') print(market_data.returns) +# Build forecasts of expected returns and covariances. +# Note that we shift so that each day we use ones built +# using past returns only. This is done automatically +# by the forecasters used by default in the stable versions +# of Cvxportfolio. +r_hat_with_cash = market_data.returns.rolling( + window=250).mean().shift(1).dropna() +Sigma_hat_without_cash = market_data.returns.iloc[:,:-1 + ].rolling(window=250).cov().shift(4).dropna() + +r_hat = r_hat_with_cash.iloc[:, :-1] +r_hat_cash = r_hat_with_cash.iloc[:,-1] +print('Expected returns forecast:') +print(r_hat_with_cash) + # Define transaction and holding cost models. + +# half spread HALF_SPREAD = 10E-4 -BORROW_FEE = 1E-4 * (252 / 100) # in annualized percentage -tcost_model = cvx.TcostModel(a=HALF_SPREAD) -hcost_model = cvx.HcostModel(short_fees=BORROW_FEE) -# As returns forecast, we simply take the historical means -# computed at each point in the back-test (looking only -# at past returns). That's the default behavior; we -# may as well pass a dataframe here with different predictions. -r_hat = cvx.ReturnsForecast() +# In the 2016 development code borrow fees were expressed per-period. +# In the stable version we require annualized percent. +# This value corresponds to 1 basis point per period, +# which was in the original example. +BORROW_FEE = 2.552 + +tcost_model = cvx.TcostModel(a=HALF_SPREAD, b=None) +hcost_model = cvx.HcostModel(short_fees=BORROW_FEE) -# As risk model, we choose the full historical covariance. -# It is computed every day using the full past historical -# returns at that point. (We may as well provide it as a -# dataframe.) -risk_model = cvx.FullSigma() +# As risk model, we use the historical covariances computed above. +# Note that the stable version of Cvxportfolio requires the covariance +# matrix to not include cash (as it should). In the development versions +# it was there. It doesn't make any difference in numerical terms. +risk_model = cvx.FullSigma(Sigma_hat_without_cash) # Constraint. leverage_limit = cvx.LeverageLimit(3) @@ -52,18 +120,19 @@ # objective function is maximized. gamma_risk, gamma_trade, gamma_hold = 5., 1., 1. spo_policy = cvx.SinglePeriodOpt( - objective = r_hat + objective = cvx.ReturnsForecast(r_hat) + cvx.CashReturn(r_hat_cash) - gamma_risk * risk_model - - gamma_trade * tcost_model + - gamma_trade * tcost_model - gamma_hold * hcost_model, - constraints=[leverage_limit]) + constraints=[leverage_limit], + include_cash_return=False) # Define the market simulator. market_sim = cvx.MarketSimulator( market_data = market_data, costs = [ - cvx.TcostModel(a=HALF_SPREAD), - cvx.HcostModel(short_fees=BORROW_FEE)]) + cvx.TcostModel(a=HALF_SPREAD, b=None), + cvx.HcostModel(short_fees=BORROW_FEE)]) # Initial portfolio, uniform on non-cash assets. init_portfolio = pd.Series( diff --git a/examples/risk_models.py b/examples/risk_models.py index 2fd255af9..8d0ee757d 100644 --- a/examples/risk_models.py +++ b/examples/risk_models.py @@ -13,9 +13,14 @@ # limitations under the License. """Test different choices of risk models, which has best performance? -**WORK IN PROGRESS** +.. note:: -On the Nasdaq 100 index, daily trading from 2016 to today: + The output of this example is currently (Cvxportfolio ``1.0.3``) + not too easy to read; the ``__repr__`` method of a policy object + with symbolic hyper-parameters is scheduled for improvement. It + does work, though. + +On the Dow Jones, daily trading from 2016 to today: - diagonal risk model - diagonal risk model with risk forecast error - full covariance @@ -24,28 +29,26 @@ We test on a long-only portfolio and use automatic hyper-parameter optimization to maximize the information ratio, in back-test, -versus an index ETF. +versus the index ETF. """ import os from pprint import pprint import matplotlib.pyplot as plt -import pandas as pd import numpy as np +import pandas as pd import cvxportfolio as cvx -from .universes import DOW30 as UNIVERSE -#from .universes import NDX100 as UNIVERSE +from .universes import DOW30 as UNIVERSE # Index -INDEX = 'DIA' # -#INDEX = 'QQQ' +INDEX = 'DIA' # Times. -START = '2000-01-01' #'2016-01-01' -END = None #'2017-01-01' +START = '2000-01-01' +END = None # today # Leverage. LEVERAGE_LIMIT = 1. @@ -60,40 +63,41 @@ all_in_index[INDEX] = 1. benchmark = cvx.FixedWeights(all_in_index) -# Define hyper-parameter objects: +# Define hyper-parameter objects. +# These will be included in the library in a future release. class GammaTradeCoarse(cvx.RangeHyperParameter): """Transaction cost multiplier, coarse value range.""" def __init__(self): super().__init__( - values_range=np.arange(1,11), + values_range=np.arange(1, 11), current_value=1.) class GammaTradeFine(cvx.RangeHyperParameter): """Transaction cost multiplier, fine value range.""" def __init__(self): super().__init__( - values_range=np.linspace(-1.,1.,51), + values_range=np.linspace(-1., 1., 51), current_value=0) class GammaRiskCoarse(cvx.RangeHyperParameter): """Risk term multiplier, coarse value range.""" def __init__(self): super().__init__( - values_range=np.arange(1,21), + values_range=np.arange(1, 21), current_value=1.) class GammaRiskFine(cvx.RangeHyperParameter): """Risk term multiplier, fine value range.""" def __init__(self): super().__init__( - values_range=np.linspace(-1.,1.,51), + values_range=np.linspace(-1., 1., 51), current_value=0) class Kappa(cvx.RangeHyperParameter): """Risk forecast error multiplier, fine value range.""" def __init__(self): super().__init__( - values_range=np.linspace(0.,0.5), + values_range=np.linspace(0., 0.5), current_value=0) @@ -132,7 +136,7 @@ def __init__(self): - (GammaTradeCoarse() + GammaTradeFine() ) * cvx.StocksTransactionCost() - risk_model, - constraints=[cvx.LongOnly(), cvx.LeverageLimit(LEVERAGE_LIMIT)], + constraints=[cvx.LongOnly(), cvx.LeverageLimit(LEVERAGE_LIMIT)], benchmark=benchmark, solver='CLARABEL') diff --git a/pyproject.toml b/pyproject.toml index 88fdaca7e..1a52c38c3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,7 @@ dependencies = ["pandas", "numpy", "matplotlib", "requests", "cvxpy", docs = ["sphinx", "furo"] dev = ["build", "twine", "coverage", "diff_cover", "pylint", "isort", "autopep8", "docformatter", "beautifulsoup4"] +examples = ['beatifulsoup4', 'pandas_datareader', 'yfinance'] [project.urls] Homepage = "https://www.cvxportfolio.com" From ba820c9f0e20d8b85ad3f33158e22901884c53c0 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Thu, 21 Dec 2023 10:24:34 +0400 Subject: [PATCH 13/16] portfolio simulation --- examples/paper_examples/data_risk_model.py | 2 +- examples/paper_examples/hello_world.py | 12 +- .../paper_examples/portfolio_simulation.py | 136 +++++++++++++++++- 3 files changed, 141 insertions(+), 9 deletions(-) diff --git a/examples/paper_examples/data_risk_model.py b/examples/paper_examples/data_risk_model.py index 70a1bbfdd..cea25ad7f 100644 --- a/examples/paper_examples/data_risk_model.py +++ b/examples/paper_examples/data_risk_model.py @@ -76,7 +76,7 @@ def paper_market_data(): Path(__file__).parent / 'returns.csv.gz', index_col=0, parse_dates=[0]) volumes = pd.read_csv( Path(__file__).parent / 'volumes.csv.gz', index_col=0, parse_dates=[0]) - print(returns) + # print(returns) return cvx.UserProvidedMarketData( returns=returns, volumes=volumes, cash_key='USDOLLAR', min_history=pd.Timedelta('0d')) diff --git a/examples/paper_examples/hello_world.py b/examples/paper_examples/hello_world.py index 6be6d3f55..ba16bbbe5 100644 --- a/examples/paper_examples/hello_world.py +++ b/examples/paper_examples/hello_world.py @@ -95,13 +95,13 @@ # Define transaction and holding cost models. -# half spread +# Half spread. HALF_SPREAD = 10E-4 # In the 2016 development code borrow fees were expressed per-period. # In the stable version we require annualized percent. -# This value corresponds to 1 basis point per period, -# which was in the original example. +# This value corresponds to 1 basis point per period, which was in the +# original example. BORROW_FEE = 2.552 tcost_model = cvx.TcostModel(a=HALF_SPREAD, b=None) @@ -109,15 +109,15 @@ # As risk model, we use the historical covariances computed above. # Note that the stable version of Cvxportfolio requires the covariance -# matrix to not include cash (as it should). In the development versions +# matrix to not include cash (as it shouldn't). In the development versions # it was there. It doesn't make any difference in numerical terms. risk_model = cvx.FullSigma(Sigma_hat_without_cash) # Constraint. leverage_limit = cvx.LeverageLimit(3) -# Define a single-period optimization policy; its -# objective function is maximized. +# Define a single-period optimization policy; its objective function is +# maximized. gamma_risk, gamma_trade, gamma_hold = 5., 1., 1. spo_policy = cvx.SinglePeriodOpt( objective = cvx.ReturnsForecast(r_hat) + cvx.CashReturn(r_hat_cash) diff --git a/examples/paper_examples/portfolio_simulation.py b/examples/paper_examples/portfolio_simulation.py index 7e7c25a12..9033cca20 100644 --- a/examples/paper_examples/portfolio_simulation.py +++ b/examples/paper_examples/portfolio_simulation.py @@ -11,6 +11,138 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""Simulate periodic rebalancing policy, analyze transaction cost. -import cvxportfolio as cvx \ No newline at end of file +This is a close translation of what was done in `this notebook +`_. + +Note that the behavior of :class:`cvxportfolio.PeriodicRebalance` changed; +We now require an explicit iterable of rebalancing timestamps. +The original implementation was smart (`you can see the code here +`_ +) but it wasn't robust enough to be included in the main library. In fact, +an API change by Pandas broke it. + +You can see in the plot that there is a slight difference in the transaction +cost model with respect to the development code. That is due to how the daily +volatility was estimated in the original examples. We used *intraday* +historical volatility, while now the library uses historical volatility of +open-to-open returns. In practice, the market impact term of the transaction +cost model is either negligible for small to medium investors, or needs to +be tuned (with the ``b`` parameter) using realized execution cost data. +""" + +import pandas as pd +import numpy as np + +import cvxportfolio as cvx + +import matplotlib.pyplot as plt +import matplotlib.ticker as mtick + +from .data_risk_model import paper_market_data, paper_risk_model + +# Start and end times of the back-test. +start_t = "2012-01-01" +end_t = "2016-12-31" + +# Short fees, in annualized percent (this is equivalent to 1bp per period, as +# it was in the notebook). +BORROW_FEE = 2.552 + +# Get market data. +market_data = paper_market_data() + +# Define benchmark weights. +w_b = pd.Series(index=market_data.returns.columns, data=1) +w_b.USDOLLAR = 0. +w_b/=sum(w_b) + +# Cost models. +simulated_tcost = cvx.TcostModel( + a=0.0005/2., b=1.) +simulated_hcost = cvx.HcostModel( + short_fees=BORROW_FEE) + +# Market simulator. +simulator = cvx.MarketSimulator(market_data = paper_market_data(), + costs=[simulated_tcost, simulated_hcost]) + +# Define policies. + +# Rebalancing times. We apply the same logic that was used in the +# development code. (It wasn't robust enough to be included in the main +# library.) +all_trading_timestamps = market_data.trading_calendar() +periodicities = ['day', 'week', 'month', 'quarter', 'year'] +rebalancing_times_per_periodicity = {} +for p in periodicities: + if p == 'week': + # Pandas dropped 'week' from the attributes of DateTimeIndex... + applied_to_ts = all_trading_timestamps.isocalendar().week.values + else: + applied_to_ts = getattr(all_trading_timestamps, p) + is_rebalancing_ts = applied_to_ts[1:] != applied_to_ts[:-1] + rebalancing_ts = all_trading_timestamps[1:][is_rebalancing_ts] + rebalancing_times_per_periodicity[p] = rebalancing_ts + +# For example, these are the annual rebalancing timestamps +print('Annual rebalancing timestamps:') +print(rebalancing_times_per_periodicity['year']) + +policies = [ + cvx.PeriodicRebalance( + target=w_b, rebalancing_times=rebalancing_times_per_periodicity[p]) + for p in periodicities] + +policies.append(cvx.Hold()) + +# Run back-tests. +res = pd.DataFrame( + index=['Daily', 'Weekly', 'Monthly', 'Quarterly', 'Annual', 'Hold']) +for label, fund_val in [(r'\$100M', 1E8), (r'\$10B', 1E10)]: + res[label] = simulator.run_multiple_backtest( + h=[fund_val*w_b] * 6, # number of policies + start_time=start_t, end_time=end_t, + policies=policies, parallel=True) + +# Compile results. +used_returns = market_data.returns.loc[ + (market_data.returns.index>=start_t)&(market_data.returns.index<=end_t)] +benchmark_returns = pd.Series( + index=used_returns.index, data=np.dot(used_returns.values, w_b.values)) + +table=pd.DataFrame() +table[r'Active return']=res.map( + lambda res: 100*250*(res.returns - benchmark_returns).mean()).unstack() +table[r'Active risk']= \ + res.map(lambda res: np.std(benchmark_returns - res.returns + )*100*np.sqrt(250)).unstack() +table[r'Trans. costs']=\ + res.map(lambda res: (res.costs['TcostModel']/res.v + ).mean() * 100 * 250).unstack() +table[r'Turnover']= \ + res.map(lambda res: res.turnover.mean()*100.*250.).unstack() + +# Print latex table. +print('Latex table:') +table_print=pd.DataFrame(table,copy=True) +table_print.iloc[:,:]=table_print.iloc[:,:].applymap(lambda x: r'%.2f%%'%x ) +print(table_print.to_latex(float_format='%.2f', escape=False).replace( + '%',r'\%')) + +# Plot. +plt.figure(figsize=(8,5)) +for v1 in table.index.levels[0][:]: + x = table.loc[v1]['Trans. costs'] + y = table.loc[v1]['Active risk'] + plt.plot(np.array(x),np.array(y), 'o-', label='$%s\mathrm{%s}$'%(v1[:-1],v1[-1:])) + +plt.legend(loc='upper right') +plt.xlabel('Transaction cost') +plt.ylabel('Risk') + +ax = plt.gca() +ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f%%')) +ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f%%')) +plt.show() \ No newline at end of file From 5ea07539dc71c7b4d98a35e6dab08e84b5c1a607 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Thu, 21 Dec 2023 14:19:04 +0400 Subject: [PATCH 14/16] market neutral example --- TODOs_ROADMAP.rst | 4 + examples/market_neutral.py | 81 +++++++++++++++++++ examples/paper_examples/hello_world.py | 26 +++--- .../paper_examples/portfolio_simulation.py | 54 ++++++------- examples/paper_examples/rank_and_spo.py | 6 +- pyproject.toml | 3 +- 6 files changed, 131 insertions(+), 43 deletions(-) create mode 100644 examples/market_neutral.py diff --git a/TODOs_ROADMAP.rst b/TODOs_ROADMAP.rst index 3c853a277..fdb7ae922 100644 --- a/TODOs_ROADMAP.rst +++ b/TODOs_ROADMAP.rst @@ -98,6 +98,10 @@ Optimization policies - [ ] Make ``BackTestResult`` interface methods with ``MarketSimulator`` public. +- [ ] Add a ``backruptcy`` property (boolean). Amend ``sharpe_ratio`` + and other aggregate statistics (as best as possible) to return ``-np.inf`` + if back-test ended in backruptcy. This is needed specifically for + hyper-parameter optimization. Target ``1.0.4``. Development & testing diff --git a/examples/market_neutral.py b/examples/market_neutral.py new file mode 100644 index 000000000..a002af903 --- /dev/null +++ b/examples/market_neutral.py @@ -0,0 +1,81 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Market (and dollar) neutral strategy on the SP500 universe. + +We use standard historical means and covariances to build a simple example +of a market neutral strategy. + +We use symbolic hyper-parameters (to be improved, see also +``examples.risk_models``) to choose the values that maximize Sharpe ratio +of the back-test, for illustrative purposes only. + +We use all default values for transaction cost (both spread and market impact +terms) and holding cost (stocks borrow fees) models. + +To improve the Sharpe ratio of such kind of strategies, in practice one would +use returns forecasts produced by some machine learning model. It is very easy +to plug such forecasts into this strategy, either by providing them as a +Dataframe or by coding the forecasting logic as a Cvxportfolio native +forecaster class, and passing either as argument to +:class:`cvxportfolio.ReturnsForecast`. +""" + +import os + +import numpy as np + +import cvxportfolio as cvx + +from .universes import NDX100 as UNIVERSE + +# Times. +START = '2016-01-01' +END = None # today + +policy = cvx.SinglePeriodOptimization( + objective=cvx.ReturnsForecast() + - cvx.Gamma() * cvx.FactorModelCovariance(num_factors=10) + - cvx.Gamma() * cvx.StocksTransactionCost() + - cvx.Gamma() * cvx.StocksHoldingCost(), + constraints = [ + cvx.DollarNeutral(), cvx.MarketNeutral(), cvx.LeverageLimit(7)], + solver='CLARABEL', + # ignore_dpp=True, # if you increase number of factors or universe size +) + +simulator = cvx.StockMarketSimulator(universe=UNIVERSE) + +simulator.optimize_hyperparameters( + policy, start_time=START, end_time=END, + objective='sharpe_ratio') + +print('Optimized policy hyper-parameters:') +print(policy) + +# Back-test the policy with optimized hyper-parameters. +result = simulator.backtest(policy, start_time=START, end_time=END) + +print("Optimized policy back-test result:") +print(result) + +# Plot. +result.plot() + +# Check that back-tested returns of the strategy are uncorrelated with the +# market benchmark. +market_benchmark_returns = simulator.backtest( + cvx.MarketBenchmark(), start_time=START, end_time=END).returns + +print('Correlation of strategy returns with benchmark:') +print(np.corrcoef(result.returns, market_benchmark_returns)[0, 1]) diff --git a/examples/paper_examples/hello_world.py b/examples/paper_examples/hello_world.py index ba16bbbe5..547199b2e 100644 --- a/examples/paper_examples/hello_world.py +++ b/examples/paper_examples/hello_world.py @@ -25,10 +25,10 @@ In this example returns and covariances are forecasted externally, while today this can be done automatically using the default forecasters -used by :class:`cvxportfolio.ReturnsForecast` and -:class:`cvxportfolio.FullCovariance`. +used by :class:`cvxportfolio.ReturnsForecast` and +:class:`cvxportfolio.FullCovariance`. -Nevertheless, you can see by running this that we are still able to +Nevertheless, you can see by running this that we are still able to reproduce exactly the behavior of the early development versions of the library. @@ -36,26 +36,24 @@ To run this, you need to install ``yfinance`` and ``pandas_datareader``. - """ import matplotlib.pyplot as plt import pandas as pd - -import yfinance import pandas_datareader as pdr +import yfinance import cvxportfolio as cvx -# Download market data +# Download market data tickers = ['AMZN', 'GOOGL', 'TSLA', 'NKE'] -returns = pd.DataFrame(dict([(ticker, +returns = pd.DataFrame(dict([(ticker, yfinance.download(ticker)['Adj Close'].pct_change()) for ticker in tickers])) - -returns["USDOLLAR"]=pdr.get_data_fred( - 'DFF', start="1900-01-01", + +returns["USDOLLAR"] = pdr.get_data_fred( + 'DFF', start="1900-01-01", end=pd.Timestamp.today())['DFF']/(252*100) returns = returns.fillna(method='ffill').iloc[1:] @@ -79,17 +77,17 @@ print(market_data.returns) # Build forecasts of expected returns and covariances. -# Note that we shift so that each day we use ones built +# Note that we shift so that each day we use ones built # using past returns only. This is done automatically # by the forecasters used by default in the stable versions # of Cvxportfolio. r_hat_with_cash = market_data.returns.rolling( window=250).mean().shift(1).dropna() -Sigma_hat_without_cash = market_data.returns.iloc[:,:-1 +Sigma_hat_without_cash = market_data.returns.iloc[:, :-1 ].rolling(window=250).cov().shift(4).dropna() r_hat = r_hat_with_cash.iloc[:, :-1] -r_hat_cash = r_hat_with_cash.iloc[:,-1] +r_hat_cash = r_hat_with_cash.iloc[:, -1] print('Expected returns forecast:') print(r_hat_with_cash) diff --git a/examples/paper_examples/portfolio_simulation.py b/examples/paper_examples/portfolio_simulation.py index 9033cca20..3a628a761 100644 --- a/examples/paper_examples/portfolio_simulation.py +++ b/examples/paper_examples/portfolio_simulation.py @@ -18,28 +18,28 @@ Note that the behavior of :class:`cvxportfolio.PeriodicRebalance` changed; We now require an explicit iterable of rebalancing timestamps. -The original implementation was smart (`you can see the code here -`_ -) but it wasn't robust enough to be included in the main library. In fact, -an API change by Pandas broke it. +The original implementation, `you can see the code here +`_, +wasn't robust enough to be included in the main library. In fact, an API change +by Pandas broke it. You can see in the plot that there is a slight difference in the transaction cost model with respect to the development code. That is due to how the daily -volatility was estimated in the original examples. We used *intraday* +volatility was estimated in the original examples. We used *intraday* historical volatility, while now the library uses historical volatility of -open-to-open returns. In practice, the market impact term of the transaction -cost model is either negligible for small to medium investors, or needs to -be tuned (with the ``b`` parameter) using realized execution cost data. +open-to-open returns, which matches what is described in the paper. In +practice, the market impact term of the transaction cost model is either +negligible for small to medium investors, or needs to be tuned (using the ``b`` +parameter) with realized execution cost data. """ -import pandas as pd +import matplotlib.pyplot as plt +import matplotlib.ticker as mtick import numpy as np +import pandas as pd import cvxportfolio as cvx -import matplotlib.pyplot as plt -import matplotlib.ticker as mtick - from .data_risk_model import paper_market_data, paper_risk_model # Start and end times of the back-test. @@ -56,7 +56,7 @@ # Define benchmark weights. w_b = pd.Series(index=market_data.returns.columns, data=1) w_b.USDOLLAR = 0. -w_b/=sum(w_b) +w_b /= sum(w_b) # Cost models. simulated_tcost = cvx.TcostModel( @@ -92,7 +92,7 @@ policies = [ cvx.PeriodicRebalance( - target=w_b, rebalancing_times=rebalancing_times_per_periodicity[p]) + target=w_b, rebalancing_times=rebalancing_times_per_periodicity[p]) for p in periodicities] policies.append(cvx.Hold()) @@ -102,41 +102,41 @@ index=['Daily', 'Weekly', 'Monthly', 'Quarterly', 'Annual', 'Hold']) for label, fund_val in [(r'\$100M', 1E8), (r'\$10B', 1E10)]: res[label] = simulator.run_multiple_backtest( - h=[fund_val*w_b] * 6, # number of policies - start_time=start_t, end_time=end_t, + h=[fund_val*w_b] * 6, # number of policies + start_time=start_t, end_time=end_t, policies=policies, parallel=True) # Compile results. used_returns = market_data.returns.loc[ - (market_data.returns.index>=start_t)&(market_data.returns.index<=end_t)] + (market_data.returns.index >= start_t)&(market_data.returns.index <= end_t)] benchmark_returns = pd.Series( index=used_returns.index, data=np.dot(used_returns.values, w_b.values)) -table=pd.DataFrame() -table[r'Active return']=res.map( +table = pd.DataFrame() +table[r'Active return'] = res.map( lambda res: 100*250*(res.returns - benchmark_returns).mean()).unstack() -table[r'Active risk']= \ +table[r'Active risk'] = \ res.map(lambda res: np.std(benchmark_returns - res.returns )*100*np.sqrt(250)).unstack() -table[r'Trans. costs']=\ +table[r'Trans. costs'] =\ res.map(lambda res: (res.costs['TcostModel']/res.v ).mean() * 100 * 250).unstack() -table[r'Turnover']= \ +table[r'Turnover'] = \ res.map(lambda res: res.turnover.mean()*100.*250.).unstack() # Print latex table. print('Latex table:') -table_print=pd.DataFrame(table,copy=True) -table_print.iloc[:,:]=table_print.iloc[:,:].applymap(lambda x: r'%.2f%%'%x ) +table_print = pd.DataFrame(table, copy=True) +table_print.iloc[:, :] = table_print.iloc[:, :].applymap(lambda x: r'%.2f%%'%x ) print(table_print.to_latex(float_format='%.2f', escape=False).replace( - '%',r'\%')) + '%', r'\%')) # Plot. -plt.figure(figsize=(8,5)) +plt.figure(figsize=(8, 5)) for v1 in table.index.levels[0][:]: x = table.loc[v1]['Trans. costs'] y = table.loc[v1]['Active risk'] - plt.plot(np.array(x),np.array(y), 'o-', label='$%s\mathrm{%s}$'%(v1[:-1],v1[-1:])) + plt.plot(np.array(x), np.array(y), 'o-', label='$%s\mathrm{%s}$'%(v1[:-1], v1[-1:])) plt.legend(loc='upper right') plt.xlabel('Transaction cost') diff --git a/examples/paper_examples/rank_and_spo.py b/examples/paper_examples/rank_and_spo.py index 7e7c25a12..2d3b9a8a3 100644 --- a/examples/paper_examples/rank_and_spo.py +++ b/examples/paper_examples/rank_and_spo.py @@ -11,6 +11,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""Ranking vs. SPO example. + +This is a close translation of what was done in `this notebook +`_. +""" import cvxportfolio as cvx \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 1a52c38c3..4275640f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,8 @@ dependencies = ["pandas", "numpy", "matplotlib", "requests", "cvxpy", docs = ["sphinx", "furo"] dev = ["build", "twine", "coverage", "diff_cover", "pylint", "isort", "autopep8", "docformatter", "beautifulsoup4"] -examples = ['beatifulsoup4', 'pandas_datareader', 'yfinance'] +examples = ['beatifulsoup4', 'pandas_datareader', 'yfinance', + 'clarabel', 'ecos'] [project.urls] Homepage = "https://www.cvxportfolio.com" From fff21e4c1260afdf1b1a734dcf8b7c8bedbc50a8 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Fri, 22 Dec 2023 16:25:24 +0400 Subject: [PATCH 15/16] regression covariance --- docs/examples/paper_examples.rst | 16 +- examples/regression_covariance.py | 274 ++++++++++++++++++++++++++++++ 2 files changed, 289 insertions(+), 1 deletion(-) create mode 100644 examples/regression_covariance.py diff --git a/docs/examples/paper_examples.rst b/docs/examples/paper_examples.rst index 8fb9277e3..5cae5a440 100644 --- a/docs/examples/paper_examples.rst +++ b/docs/examples/paper_examples.rst @@ -1,6 +1,20 @@ Original examples from the paper ================================ +These are the original examples, which were also used to generate plots +and results in :paper:`chapter 7 of the paper `. They were +originally written as IPython notebook, and have been translated into Python +code using the stable API of Cvxportfolio (*i.e.*, version ``1.0.0`` and +above). + +The original notebooks are `available on this branch of the repository +`_. + +.. note:: + + Currently some of them are *work in progress*, refer to the linked notebook + if in doubt. + .. toctree:: :maxdepth: 2 @@ -13,4 +27,4 @@ Original examples from the paper paper_examples/single_period_opt paper_examples/single_period_opt_lin_tcost paper_examples/solution_time - \ No newline at end of file + diff --git a/examples/regression_covariance.py b/examples/regression_covariance.py new file mode 100644 index 000000000..15c140034 --- /dev/null +++ b/examples/regression_covariance.py @@ -0,0 +1,274 @@ +# Copyright 2023 Enzo Busseti +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Build covariance matrix by regression. + +This is a prototype for a planned extension of the forecast module in the main +library. However we're not sure yet which precise form it will take. + + +.. note:: + + The internal interface methods used here **may not be public** + and may not covered by the semantic versioning agreement (they might change + without notice). In general, methods that are shown in the examples are + considered public, but for this one it may not be not so. + +""" + +import numpy as np +import pandas as pd + +import cvxportfolio as cvx + +from cvxportfolio.estimator import Estimator +# from cvxportfolio.forecast import BaseForecast + +def _striptime(df): + res = pd.DataFrame(df) + res.index = pd.to_datetime(df.index.date) + return res + +def _covariance(returns): + _ = returns.fillna(0.) + num = _.T @ _ + _ = (~returns.isnull()) * 1. + den = _.T @ _ + return num / den + +def _covariance_weigh(returns, weighter): + tmp = returns.multiply(weighter, axis=0) + left = tmp.fillna(0.) + right = returns.multiply(~(weighter.isnull())*1., axis=0).fillna(0.) + num = left.T @ right + _ = (~tmp.isnull()) * 1. + den = _.T @ _ + return num / den + +def _decorrelate(XtX, obs, gamma): + return np.linalg.solve( + XtX + np.diag(([gamma] * (len(obs)-1)) + [0.]), + obs) + +class RegressionBase(Estimator): + """Base for linear regression forecasters. + """ + + def __init__(self, regressors, gamma=1e-1): + """Initialize with regressors. + + For simplicity here we drop time (and keep date) from the index. + + :param regressors: Regressors used. + :type regressors: pandas.DataFrame + :param gamma: Tikhonov regularizer, default 0. + :type gamma: float + """ + self.regressors = _striptime(regressors) + self.regressors['intercept'] = 1. + self.gamma = gamma + + def _obtain_weighted(self, used_regressors, **kwargs): + """Obtain objects weighted by the regressors. + + :param used_regressors: Used regressors (only past). + :type used_regressors: pandas.DataFrame + :param kwargs: All arguments to :meth:`values_in_time`. + :type kwargs: dict + """ + + raise NotImplementedError + + def values_in_time(self, t, past_returns, **kwargs): + """Produce regressed object for the period. + + :param t: Current time. + :type t: pandas.Timestamp + :param past_returns: Past returns (includes cash). + :type past_returns: pandas.DataFrame + :param kwargs: Other arguments. + :type kwargs: dict + + :return: Regressed covariance. + :rtype: pandas.DataFrame + """ + # raise Exception + used_regressors = pd.DataFrame( + self.regressors.loc[ + self.regressors.index <= pd.Timestamp(t.date())], copy=True) + + # to extend to index + used_regressors['intercept'] = past_returns.iloc[:,-1] + + used_regressors = used_regressors.ffill() + used_regressors -= used_regressors.mean() + used_regressors /= np.sqrt((used_regressors**2).mean()) + + used_regressors['intercept'] = 1. + + # print('used_regressors') + # print(used_regressors) + + _ = _covariance(used_regressors.iloc[:-1]) + # print('XtX', _) + lhs = _decorrelate(_, used_regressors.iloc[-1], gamma=self.gamma) + # print('lhs', lhs) + + _ = self._obtain_weighted(used_regressors, + t=t, past_returns=past_returns, **kwargs) + + return sum(el * _[i] for i, el in enumerate(lhs)) + + +class RegressionCovariance(RegressionBase): + """Covariance matrix that is obtained by linear regression. + """ + + def _obtain_weighted(self, used_regressors, past_returns, **kwargs): + """Obtain objects weigthed by the regressors. + + :param used_regressors: Used regressors (only past). + :type used_regressors: pandas.DataFrame + :param past_returns: Past market returns. + :type past_returns: pandas.DataFrame + :param kwargs: All other arguments to :meth:`values_in_time`. + :type kwargs: dict + """ + + # creates new object, doesn't damage original one + past_returns = _striptime(past_returns) + + return [_covariance_weigh( + past_returns.iloc[:, :-1], used_regressors.iloc[:-1][col]) + for col in used_regressors] + +class RegressionReturns(RegressionBase): + """Returns forecast that is obtained by linear regression. + """ + + def _obtain_weighted(self, used_regressors, past_returns, **kwargs): + """Obtain objects weighted by the regressors. + + :param used_regressors: Used regressors (only past). + :type used_regressors: pandas.DataFrame + :param past_returns: Past market returns. + :type past_returns: pandas.DataFrame + :param kwargs: All other arguments to :meth:`values_in_time`. + :type kwargs: dict + """ + + # creates new object, doesn't damage original one + past_returns = _striptime(past_returns) + + return [ past_returns.iloc[:, :-1].multiply( + used_regressors.iloc[:-1][col], axis=0).mean().values + for col in used_regressors] + +def example_regressors(): + """Vix regressors and others. + + The open value of VIX is avalaible at market open US, so this is a valid + (no look-ahead) set of regressors. + + :return: Various regressors, with different timescales and availability. + :rtype: pandas.DataFrame + """ + vix = cvx.YahooFinance('^VIX').data.open + rate = cvx.DownloadedMarketData(['GE']).returns.USDOLLAR + #wage_change = cvx.Fred('CES0500000003').data.pct_change() + #gdp_change = cvx.Fred('GDP').data.pct_change() + base_regressors = { + 'vix':vix, + 'rate':rate, + #'wage_change': wage_change, + #'gdp_change': gdp_change + } + regressors = pd.DataFrame(base_regressors) + + # for col in base_regressors: + # if np.all(regressors[col].dropna() > 0): + # regressors['log_' + col] = np.log(regressors[col]) + # regressors['square_'+ col] = regressors[col]**2 + # regressors['cube_' + col] = regressors[col]**3 + + return _striptime(regressors).ffill() + +if __name__ == '__main__': + + from .universes import DOW30 + + REGRESSORS = example_regressors() + print(REGRESSORS) + + # RETURNS_REGRESSORS = REGRESSORS[ + # [col for col in REGRESSORS.columns + # if not 'vix' in col] + # ] + + # market_data = cvx.DownloadedMarketData(['AAPL', 'GOOG', 'TSLA']) + + cov = RegressionCovariance(REGRESSORS, gamma = 1e-6) + # ret = RegressionReturns(RETURNS_REGRESSORS, gamma = 1000) + + # tmp = pd.DataFrame(market_data.returns.iloc[:-10],copy=True) + # tmp.iloc[-1] = np.nan + #print(cov.values_in_time( + # t = market_data.returns.index[-10], + # past_returns=tmp,)) + + constraints = [ + #cvx.LongOnly(), + cvx.LeverageLimit(7) + ] + + # result_plain, result_covregr, result_retregr, result_retcovregr = \ + result_plain, result_covregr = \ + cvx.MarketSimulator(DOW30).backtest_many( + [ + cvx.SinglePeriodOptimization(cvx.ReturnsForecast() + - 3 * cvx.FullCovariance(), + constraints, + ), + cvx.SinglePeriodOptimization(cvx.ReturnsForecast() + - 3 * cvx.FullCovariance(cov), + constraints, + ), + # cvx.SinglePeriodOptimization(cvx.ReturnsForecast(ret) + # - 3 * cvx.FullCovariance(), + # constraints, + # ), + # cvx.SinglePeriodOptimization(cvx.ReturnsForecast(ret) + # - 3 * cvx.FullCovariance(cov), + # constraints, + # ), + ], + start_time='2010-01-01') # can't do earlier than ~2010 + # because regressor not available, + # need to add logic to include them after some min_history + + result_plain.plot() + result_covregr.plot() + # result_retregr.plot() + # result_retcovregr.plot() + + print('Result with no regression:') + print(result_plain) + + print('Result with covariance regression:') + print(result_covregr) + + # print('Result with returns regression:') + # print(result_retregr) + + # print('Result with returns and covariance regression:') + # print(result_retcovregr) \ No newline at end of file From 75a3e428ef0b352a92be81b91bb891b6fb4dfca2 Mon Sep 17 00:00:00 2001 From: Enzo Busseti Date: Sun, 24 Dec 2023 23:28:26 +0400 Subject: [PATCH 16/16] work in progress added --- README.rst | 17 ++++++----- docs/examples/paper_examples.rst | 2 ++ examples/market_neutral.py | 12 ++++---- examples/paper_examples/hello_world.py | 2 +- examples/paper_examples/multi_period_opt.py | 2 +- examples/paper_examples/rank_and_spo.py | 2 ++ .../paper_examples/real_time_optimization.py | 2 +- examples/paper_examples/single_period_opt.py | 2 +- .../single_period_opt_lin_tcost.py | 2 +- examples/paper_examples/solution_time.py | 2 +- examples/regression_covariance.py | 30 ++++++++----------- 11 files changed, 39 insertions(+), 36 deletions(-) diff --git a/README.rst b/README.rst index 543efd6f6..9b69d2345 100644 --- a/README.rst +++ b/README.rst @@ -21,7 +21,7 @@ Installation Cvxportolio is written in Python and can easily installed in any Python environment by simple: -.. code:: console +.. code:: bash pip install -U cvxportfolio @@ -43,7 +43,7 @@ Test After installing you can run our unit test suite in you local environment by -.. code:: console +.. code:: bash python -m cvxportfolio.tests @@ -109,8 +109,9 @@ are available in the code repository. `The original examples from the paper `_ are visible in a dedicated branch, -and are being translated to run with the stable versions (``1.0.*``) of the -library. +and are being translated to run with the stable versions (``1.0.0`` and above) of the +library. The translations are visible at `this documentation page +_`. We show in the example on `user-provided forecasters `__ @@ -156,7 +157,7 @@ repository (or, `fork on Github `__ and then clone your fork) -.. code:: console +.. code:: bash git clone https://github.com/cvxgrp/cvxportfolio.git cd cvxportfolio @@ -166,7 +167,7 @@ Then, you should have a look at our and possibly change the ``PYTHON`` variable to match your system’s python interpreter. Once you have done that, -.. code:: console +.. code:: bash make env make test @@ -178,7 +179,7 @@ test suite. You activate the shell environment with one of scripts in ``env/bin`` (or ``env\Scripts`` on Windows), for example if you use bash on POSIX -.. code:: console +.. code:: bash source env/bin/activate @@ -192,7 +193,7 @@ Windows) like we do in the Makefile. Additionally, to match our CI/CD pipeline, you may set the following `git hooks `__ -.. code:: console +.. code:: bash echo "make lint" > .git/hooks/pre-commit chmod +x .git/hooks/pre-commit diff --git a/docs/examples/paper_examples.rst b/docs/examples/paper_examples.rst index 5cae5a440..32769f8c1 100644 --- a/docs/examples/paper_examples.rst +++ b/docs/examples/paper_examples.rst @@ -1,6 +1,8 @@ Original examples from the paper ================================ +*Work in progress.* + These are the original examples, which were also used to generate plots and results in :paper:`chapter 7 of the paper `. They were originally written as IPython notebook, and have been translated into Python diff --git a/examples/market_neutral.py b/examples/market_neutral.py index a002af903..8b01efe07 100644 --- a/examples/market_neutral.py +++ b/examples/market_neutral.py @@ -11,19 +11,21 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Market (and dollar) neutral strategy on the SP500 universe. +"""Market (and dollar) neutral strategy on the NDX100 universe. -We use standard historical means and covariances to build a simple example -of a market neutral strategy. +We use standard historical means and factor covariances to build a simple +example of a market neutral strategy. We use symbolic hyper-parameters (to be improved, see also ``examples.risk_models``) to choose the values that maximize Sharpe ratio of the back-test, for illustrative purposes only. We use all default values for transaction cost (both spread and market impact -terms) and holding cost (stocks borrow fees) models. +terms) and holding cost (stocks borrow fees) models. These are used both for +the optimization and the back-test in +:class:`cvxportfolio.StockMarketSimulator`. -To improve the Sharpe ratio of such kind of strategies, in practice one would +To improve the Sharpe ratio of this kind of strategies, in practice, one would use returns forecasts produced by some machine learning model. It is very easy to plug such forecasts into this strategy, either by providing them as a Dataframe or by coding the forecasting logic as a Cvxportfolio native diff --git a/examples/paper_examples/hello_world.py b/examples/paper_examples/hello_world.py index 547199b2e..3e5cd8964 100644 --- a/examples/paper_examples/hello_world.py +++ b/examples/paper_examples/hello_world.py @@ -32,7 +32,7 @@ reproduce exactly the behavior of the early development versions of the library. -.. note :: +.. note:: To run this, you need to install ``yfinance`` and ``pandas_datareader``. diff --git a/examples/paper_examples/multi_period_opt.py b/examples/paper_examples/multi_period_opt.py index 7e7c25a12..73e9a829b 100644 --- a/examples/paper_examples/multi_period_opt.py +++ b/examples/paper_examples/multi_period_opt.py @@ -11,6 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""*Work in progress.*""" import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/rank_and_spo.py b/examples/paper_examples/rank_and_spo.py index 2d3b9a8a3..be7f1bdf3 100644 --- a/examples/paper_examples/rank_and_spo.py +++ b/examples/paper_examples/rank_and_spo.py @@ -13,6 +13,8 @@ # limitations under the License. """Ranking vs. SPO example. +*Work in progress.* + This is a close translation of what was done in `this notebook `_. """ diff --git a/examples/paper_examples/real_time_optimization.py b/examples/paper_examples/real_time_optimization.py index 7e7c25a12..73e9a829b 100644 --- a/examples/paper_examples/real_time_optimization.py +++ b/examples/paper_examples/real_time_optimization.py @@ -11,6 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""*Work in progress.*""" import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/single_period_opt.py b/examples/paper_examples/single_period_opt.py index 7e7c25a12..73e9a829b 100644 --- a/examples/paper_examples/single_period_opt.py +++ b/examples/paper_examples/single_period_opt.py @@ -11,6 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""*Work in progress.*""" import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/single_period_opt_lin_tcost.py b/examples/paper_examples/single_period_opt_lin_tcost.py index 7e7c25a12..73e9a829b 100644 --- a/examples/paper_examples/single_period_opt_lin_tcost.py +++ b/examples/paper_examples/single_period_opt_lin_tcost.py @@ -11,6 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""*Work in progress.*""" import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/paper_examples/solution_time.py b/examples/paper_examples/solution_time.py index 7e7c25a12..73e9a829b 100644 --- a/examples/paper_examples/solution_time.py +++ b/examples/paper_examples/solution_time.py @@ -11,6 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""""" +"""*Work in progress.*""" import cvxportfolio as cvx \ No newline at end of file diff --git a/examples/regression_covariance.py b/examples/regression_covariance.py index 15c140034..e7f0e72c9 100644 --- a/examples/regression_covariance.py +++ b/examples/regression_covariance.py @@ -19,19 +19,18 @@ .. note:: - The internal interface methods used here **may not be public** + The internal interface methods used here **may not be public** and may not covered by the semantic versioning agreement (they might change without notice). In general, methods that are shown in the examples are - considered public, but for this one it may not be not so. - + considered public, but for this one there is no such guarantee. """ import numpy as np import pandas as pd import cvxportfolio as cvx - from cvxportfolio.estimator import Estimator + # from cvxportfolio.forecast import BaseForecast def _striptime(df): @@ -61,8 +60,7 @@ def _decorrelate(XtX, obs, gamma): obs) class RegressionBase(Estimator): - """Base for linear regression forecasters. - """ + """Base for linear regression forecasters.""" def __init__(self, regressors, gamma=1e-1): """Initialize with regressors. @@ -108,8 +106,8 @@ def values_in_time(self, t, past_returns, **kwargs): self.regressors.index <= pd.Timestamp(t.date())], copy=True) # to extend to index - used_regressors['intercept'] = past_returns.iloc[:,-1] - + used_regressors['intercept'] = past_returns.iloc[:, -1] + used_regressors = used_regressors.ffill() used_regressors -= used_regressors.mean() used_regressors /= np.sqrt((used_regressors**2).mean()) @@ -124,15 +122,14 @@ def values_in_time(self, t, past_returns, **kwargs): lhs = _decorrelate(_, used_regressors.iloc[-1], gamma=self.gamma) # print('lhs', lhs) - _ = self._obtain_weighted(used_regressors, + _ = self._obtain_weighted(used_regressors, t=t, past_returns=past_returns, **kwargs) return sum(el * _[i] for i, el in enumerate(lhs)) class RegressionCovariance(RegressionBase): - """Covariance matrix that is obtained by linear regression. - """ + """Covariance matrix that is obtained by linear regression.""" def _obtain_weighted(self, used_regressors, past_returns, **kwargs): """Obtain objects weigthed by the regressors. @@ -153,8 +150,7 @@ def _obtain_weighted(self, used_regressors, past_returns, **kwargs): for col in used_regressors] class RegressionReturns(RegressionBase): - """Returns forecast that is obtained by linear regression. - """ + """Returns forecast that is obtained by linear regression.""" def _obtain_weighted(self, used_regressors, past_returns, **kwargs): """Obtain objects weighted by the regressors. @@ -188,9 +184,9 @@ def example_regressors(): #wage_change = cvx.Fred('CES0500000003').data.pct_change() #gdp_change = cvx.Fred('GDP').data.pct_change() base_regressors = { - 'vix':vix, - 'rate':rate, - #'wage_change': wage_change, + 'vix': vix, + 'rate': rate, + #'wage_change': wage_change, #'gdp_change': gdp_change } regressors = pd.DataFrame(base_regressors) @@ -255,7 +251,7 @@ def example_regressors(): start_time='2010-01-01') # can't do earlier than ~2010 # because regressor not available, # need to add logic to include them after some min_history - + result_plain.plot() result_covregr.plot() # result_retregr.plot()

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