#!/usr/bin/env python
# coding: utf-8

# # Performance Hedge
# 
# The Performance Hedge provides a framework to replicate an asset's performance without
# direct exposure by using historical performance correlation.
# 
# ## Step 1: Authenticate and Initialize Your Session
# 
# First you will import the necessary modules and add your client id and client secret.

# In[ ]:


import datetime as dt

from IPython.display import display

from gs_quant.markets.hedge import HedgeConstraints, HedgeExclusions, Constraint, PerformanceHedge, \
    PerformanceHedgeParameters
from gs_quant.markets.position_set import Position, PositionSet
from gs_quant.session import GsSession, Environment
from gs_quant.timeseries.helper import Window
from gs_quant.timeseries.econometrics import correlation

# external users should substitute their client id and secret; please skip this step if using internal jupyterhub
GsSession.use(Environment.PROD, client_id=None, client_secret=None)

print('GS Session initialized.')


# ## Step 2: Define Your Positions to Hedge and Your Hedge Universe
# 
# The hedger takes in the initial portfolio as a `PositionSet` object. You can define your positions
# as a list of identifiers with quantities or alternatively, as a list of identifiers with weights, along with a
# reference notional value. The `date` corresponds to the hedge date.
# 
# In addition, you need to define your universe of candidates for hedge constituents as a list of identifiers.
# 
# *GS Quant will resolve all identifiers (Bloomberg IDs, SEDOLs, RICs, etc) historically as of the hedge date*

# In[ ]:


positions = PositionSet(
        date=dt.date(day=24, month=9, year=2021),
        positions=[
            Position(identifier='AAPL UW', quantity=26),
            Position(identifier='GS UN', quantity=51)
        ]
    )

positions.resolve()

universe = ['SPX']


# ## Step 3: Define Your Hedge Exclusions
# 
# The `HedgeExclusions` class offers a clean way to specify any assets, countries, regions, sectors, and/or industries you
# would like to exclude from your hedge. Each attribute takes in a list of strings. In this example, let's try excluding
# Goldman Sachs and all assets in Mexico, Europe, the Utilities sector, the Airlines industry.

# In[ ]:


exclusions = HedgeExclusions(assets=['GS UN'],
                             countries=['Mexico'],
                             regions=['Europe'],
                             sectors=['Utilities'],
                             industries=['Airlines'])


# ## Step 4: Define Your Hedge Constraints
# 
# Rather than excluding an industry or region entirely, you can also constrain how much each makes up in
# your hedge, and you can do so by leveraging the `HedgeConstraints` class. Each attribute takes in a list of `Constraint`
# objects, each of which has a `name` attribute, along with `minimum` and `maximum` that should be expressed as positive
# numbers between 0 and 100.
# 
# In addition to constraining assets, countries, regions, sectors, and industries, it's also
# possible to constrain your hedge to only include assets that have an ESG score between a specified range. All data is
# pulled as of your hedge date from the GIR SUSTAIN ESG Headline Metrics Dataset. For more information on the various
# ESG metrics available, please visit the dataset page
# [here](https://marquee.gs.com/s/developer/datasets/ESG_HEADLINE_METRICS).
# 
# In this example, let's constrain our hedge to only include at most 20% Software assets by weight. In addition, let's
# request a hedge with only assets that have a G Headline Percentile Score of above 75%.

# In[ ]:


constraints = HedgeConstraints(sectors=[Constraint(constraint_name='Software', minimum=0, maximum=20)],
                               esg=[Constraint(constraint_name='gPercentile', minimum=75, maximum=100)])


# ## Step 5: Define Any Other Parameters
# 
# The `PerformanceHedgeParameters` wraps all the performance hedge parameters, including the positions, exclusions, and
# constraints, into an object to be passed into a `PerformanceHedge`. Along with the parameters defined above, the
# following optional parameters can also be passed in:
# 
# | Parameter | Description | Type| Default Value|
# |-----------------|---------------|-------------|-------------
# | `observation_start_date`      | Date on which to start the observation of historical performance correlation |`datetime.date`| One year before the hedge date |
# | `sampling_period`     | The length of time in between return samples |`str`| 'Daily' |
# | `max_leverage`       | Maximum percentage of the notional that can be used to hedge | `float` | 100 |
# | `percentage_in_cash`       | Percentage of the hedge notional that will be in cash | `float` | None |
# | `explode_universe`       | Explode the assets in the universe into their underliers to be used as the hedge universe | `boolean` | True |
# | `exclude_target_assets`      | Exclude assets in the target composition from being in the hedge |`boolean`| True |
# | `exclude_corporate_actions_types`     | Set of of corporate actions to be excluded in the hedge |`List[CorporateActionsTypes]`| None |
# | `exclude_hard_to_borrow_assets`       | Whether hard to borrow assets should be excluded in the universe | `boolean` | False |
# | `exclude_restricted_assets`       | Whether to exclude assets in restricted trading lists | `float` | False |
# | `max_adv_percentage`       | Maximum percentage notional to average daily dollar volume allowed for any hedge constituent | `float` | 15 |
# | `max_return_deviation`      | Maximum percentage difference in annualized return between the target and the hedge result |`float`| 5 |
# | `max_weight`     | Maximum weight of any constituent in hedge |`float`| 100 |
# | `min_market_cap`       | Lowest market cap allowed for any hedge constituent | `float` | None |
# | `max_market_cap`       | Highest market cap allowed for any hedge constituent | `float` | None |
# | `market_participation_rate`       | Maximum market participation rate used to estimate the cost of trading a portfolio of stocks | `float` | 10 |
# | `lasso_weight`      | Value of the lasso hyperparameter for machine learning hedges |`float`| 0 |
# | `ridge_weight`      | Value of the ridge hyperparameter for machine learning hedges |`float`| 0 |
# 

# In[ ]:


parameters = PerformanceHedgeParameters(
    initial_portfolio=positions,
    universe=universe,
    exclusions=exclusions,
    constraints=constraints
)


# ## Step 6: Calculate Your Hedge
# 
# It's finally time to run the parameters into the Marquee Hedger. Once defined, a `PerformanceHedge` can be calculated
# in just one line.
# 

# In[ ]:


hedge = PerformanceHedge(parameters)
all_results = hedge.calculate()


# ## Step 7: Pull Hedge Results
# 
# That's it! Once calculated, you can pull the results right from the `PerformanceHedge` object.
# 
# ### Constituents
# Let's pull the constituents metadata of the resulting hedge:
# 

# In[ ]:


hedge_constituents = hedge.get_constituents()
display(hedge_constituents)


# ### Stats
# 
# Next let's pull a table of general stats like transaction cost, annualized volatility, annualized return, and more:
# 

# In[ ]:


stats = hedge.get_statistics()
display(stats)


# ### Backtest Performance
# 
# It's also possible to pull a timeseries of the performance of both the initial portfolio and the hedge for the
# observation period:
# 

# In[ ]:


backtest_performance = hedge.get_backtest_performance()

backtest_performance.plot(title='Backtest Performance')


# ### Backtest Correlation
# 
# It's also possible to pull a timeseries of the correlation between the hedge and portfolio for the
# observation period by leveraging GS Quant econometric function `correlation`:
# 

# In[ ]:


backtest_correlation = correlation(backtest_performance['Portfolio'], backtest_performance['Hedge'], Window(44, 0))
backtest_correlation.plot(title='Backtest Correlation')


# ### You're all set; Congrats!
# 
# *Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*