Multi-Factor Least Squares Monte Carlo energy storage valuation model. Usable from C#, Python and Excel.
- Overview
- Models Implemented
- Getting Started
- .NET Dependency
- Using the Python API
- Using the C# API
- Using the Excel Add-In
- Calibration
- Building
- Why the Strange Tech Stack?
- Debugging C# Code From a Jupyter Notebook
- Get in Touch and/or Give a Star
- License
A collection of models for the valuation and optimisation of commodity storage, either virtual or physical. The models can be used for any commodity, although are most suitable for natural gas storage valuation and optimisation.
Calculations take into account many of the complex features of physical storage including:
- Inventory dependent injection and withdrawal rates, otherwise known as ratchets. For physical storage it is often the case that maximum withdrawal rates will increase, and injection rates will decrease as the storage inventory increases. For natural gas, this due to the increased pressure within the storage cavern.
- Time dependent injection and withdrawal rates, including the ability to add outages when no injection or withdrawal is allowed.
- Forced injection/withdrawal, as can be enforced by regulatory or physical constraints.
- Commodity consumed on injection/withdrawal, for example where natural gas is consumed by the motors that power injection into storage.
- Time dependent minimum and maximum inventory, necessary if different notional volumes of a storage facility are leased for different consecutive years.
- Optional time and inventory dependent loss of commodity in storage. For example this assumption is necessary for electricity storage which isn't 100% efficient.
- Ability to constrain the storage to be empty at the end of it's life, or specify a value of commodity inventory left in storage.
Currently the following models are implemented in this repository:
- Intrinsic valuation, i.e. optimal value assuming the commodity price remains static.
- One-factor trinomial tree, with seasonal spot volatility.
- Least-Squares Monte Carlo with a multi-factor price process including the flexibility for callers to provide own price simulations.
For use from C# install the NuGet package Cmdty.Storage.
PM> Install-Package Cmdty.Storage
> pip install cmdty-storage
If running on an OS other than Windows see the section .NET Dependency below.
- First determine if your installed Excel is 32-bit or 64-bit. A Google search can tell you how to do this.
- Download the Excel add-in zip file from the releases page for the latest Excel release.
- If your Excel is 32-bit, download Cmdty.Storage-x86.zip.
- If your Excel is 64-bit, download Cmdty.Storage-x64.zip.
- Create a folder on your local drive to hold the add-in files. You might want to create this within a folder specifically to hold Excel add-ins.
- Unzip the contents of the zip file into the folder created in the previous step. Some issues have been observed during development with anti-malware software flagging the .xll as malicious. A similar warning could occur at this step of installation. This is a known issue with Excel-DNA. Please raise an issue in this repo if this happens.
- Open Excel and go to the File > Options dialogue box.
- Open the Add-ins tab on the left. At the bottom there is “Manage:” label next to a drop-down which should be selected to “Excel Add-ins”. Press the Go button next to this. A new dialogue box will open.
- Press the Browse button which should open a file selector dialogue box in which you should select the Cmdty.Storage xll file which was previously saved to your local disk. Press OK.
- This should take you back to the previous Add-ins dialogue box where you should press OK.
- Back in Excel you can confirm that the add-in has been installed by:
- In the Add-ins tab of the Ribbon there should now be a drop-down menu labelled "Cmdty.Storage".
- New Excel functions prefixed with "cmdty." should be available. These can be seen in the Excel Insert Function dialogue box, within the Cmdty.Storage category.
As Cmdty.Storage is mostly written in C# it requires the .NET runtime to be installed to execute. The dlls are targetting .NET Standard 2.0 which is compatible with .NET Framework versions 4.6.1 upwards. A version of .NET Framework meeting this restriction should be installed on most Windows computers, so nothing extra is required.
If running on a non-Windows OS then the runtime of a cross-platform type of .NET will be required. .NET Standard is compatible with .NET and Mono, with the former being recommended. For the Python package, by default it will try to use .NET, and if this isn't installed it will try Mono. See the Microsoft documentation on installing the .NET runtime on Linux and on macOS.
The first step is to create an instance of the class CmdtyStorage which represents the storage facility. This section gives two examples of how to do this. For full details on how to create CmdtyStorage instances see the Jupyter notebook creating_storage_instances.ipynb.
The following code creates a simple storage object with constant constraints.
from cmdty_storage import CmdtyStorage, RatchetInterp
import pandas as pd
storage_simple = CmdtyStorage(
freq='D',
storage_start = '2021-04-01',
storage_end = '2022-04-01',
injection_cost = 0.01,
withdrawal_cost = 0.025,
min_inventory = 0.0,
max_inventory = 1500.0,
max_injection_rate = 25.5,
max_withdrawal_rate = 30.9
)The second examples creates a storage object with inventory-varying injection and withdrawal rates, commonly known as "ratchets".
storage_with_ratchets = CmdtyStorage(
freq='D',
storage_start = '2021-04-01',
storage_end = '2022-04-01',
injection_cost = 0.01,
withdrawal_cost = 0.025,
ratchets = [
('2021-04-01', # For days after 2021-04-01 (inclusive) until 2022-10-01 (exclusive):
[
(0.0, -150.0, 250.0), # At min inventory of zero, max withdrawal of 150, max injection 250
(2000.0, -200.0, 175.0), # At inventory of 2000, max withdrawal of 200, max injection 175
(5000.0, -260.0, 155.0), # At inventory of 5000, max withdrawal of 260, max injection 155
(7000.0, -275.0, 132.0), # At max inventory of 7000, max withdrawal of 275, max injection 132
]),
('2022-10-01', # For days after 2022-10-01 (inclusive):
[
(0.0, -130.0, 260.0), # At min inventory of zero, max withdrawal of 130, max injection 260
(2000.0, -190.0, 190.0), # At inventory of 2000, max withdrawal of 190, max injection 190
(5000.0, -230.0, 165.0), # At inventory of 5000, max withdrawal of 230, max injection 165
(7000.0, -245.0, 148.0), # At max inventory of 7000, max withdrawal of 245, max injection 148
]),
],
ratchet_interp = RatchetInterp.LINEAR
)The following is an example of valuing the storage using LSMC and a three-factor seasonal model of price dynamics. For comprehensive documentation of invoking the LSMC model, using the three-factor price model, a more general multi-factor model, or externally generated price simulations, see the notebook multifactor_storage.ipynb.
from cmdty_storage import three_factor_seasonal_value
# Creating the Inputs
monthly_index = pd.period_range(start='2021-04-25', periods=25, freq='M')
monthly_fwd_prices = [16.61, 15.68, 15.42, 15.31, 15.27, 15.13, 15.96, 17.22, 17.32, 17.66,
17.59, 16.81, 15.36, 14.49, 14.28, 14.25, 14.32, 14.33, 15.30, 16.58,
16.64, 16.79, 16.64, 15.90, 14.63]
fwd_curve = pd.Series(data=monthly_fwd_prices, index=monthly_index).resample('D').fillna('pad')
rates = [0.005, 0.006, 0.0072, 0.0087, 0.0101, 0.0115, 0.0126]
rates_pillars = pd.PeriodIndex(freq='D', data=['2021-04-25', '2021-06-01', '2021-08-01', '2021-12-01', '2022-04-01',
'2022-12-01', '2023-12-01'])
ir_curve = pd.Series(data=rates, index=rates_pillars).resample('D').asfreq('D').interpolate(method='linear')
def settlement_rule(delivery_date):
return delivery_date.asfreq('M').asfreq('D', 'end') + 20
# Call the three-factor seasonal model
three_factor_results = three_factor_seasonal_value(
cmdty_storage = storage_with_ratchets,
val_date = '2021-04-25',
inventory = 1500.0,
fwd_curve = fwd_curve,
interest_rates = ir_curve,
settlement_rule = settlement_rule,
num_sims = 2000,
seed = 12,
spot_mean_reversion = 91.0,
spot_vol = 0.85,
long_term_vol = 0.30,
seasonal_vol = 0.19,
basis_funcs = '1 + x_st + x_sw + x_lt + s + x_st**2 + x_sw**2 + x_lt**2 + s**2 + s * x_st',
discount_deltas = True
)
# Inspect the NPV results
print('Full NPV:\t{0:,.0f}'.format(three_factor_results.npv))
print('Intrinsic NPV: \t{0:,.0f}'.format(three_factor_results.intrinsic_npv))
print('Extrinsic NPV: \t{0:,.0f}'.format(three_factor_results.extrinsic_npv))Prints the following.
Full NPV: 78,175
Intrinsic NPV: 40,976
Extrinsic NPV: 37,199
The object returned from the calling three_factor_seasonal_value has many properties containing useful information. The code below give examples of a
few of these. See the Valuation Results section of multifactor_storage.ipynb for more details.
Plotting the daily Deltas and projected inventory:
%matplotlib inline
ax_deltas = three_factor_results.deltas.plot(title='Daily Deltas vs Projected Inventory', legend=True, label='Delta')
ax_deltas.set_ylabel('Delta')
inventory_projection = three_factor_results.expected_profile['inventory']
ax_inventory = inventory_projection.plot(secondary_y=True, legend=True, ax=ax_deltas, label='Expected Inventory')
h1, l1 = ax_deltas.get_legend_handles_labels()
h2, l2 = ax_inventory.get_legend_handles_labels()
ax_inventory.set_ylabel('Inventory')
ax_deltas.legend(h1+h2, l1+l2, loc=1)
The trigger_prices property contains information on "trigger prices" which are approximate spot price levels at which the exercise decision changes.
- The withdraw trigger price is the spot price level, at time of nomination, above which the optimal decision will change to withdraw.
- The inject trigger price is the spot price level, at time of nomination, below which the optimal decision will change to inject.
Plotting the trigger prices versus the forward curve:
%matplotlib inline
ax_triggers = three_factor_results.trigger_prices['inject_trigger_price'].plot(
title='Trigger Prices vs Forward Curve', legend=True)
three_factor_results.trigger_prices['withdraw_trigger_price'].plot(legend=True)
fwd_curve['2021-04-25' : '2022-04-01'].plot(legend=True)
ax_triggers.legend(['Inject Trigger Price', 'Withdraw Trigger', 'Forward Curve'])
The following (currently undocumented) Python classes provide helper functionality around the multi-factor model:
- MultiFactorModel calculates forward covariances given multi-factor parameters. This can be used to understand the dynamics of the model for various purposes, including calibration.
- MultiFactorSpotSim simulations the spot prices using the multi-factor model. This can be used to build other Monte Carlo models.
An example GUI notebook created using Jupyter Widgets can be found here.
In some environments the valuation calculations have been observed to crash the Python
interpretter. This is due to the use of Intel MKL, which itself loads libiomp5md.dll, the OpenMP threading library.
The crash occurs during the initialisation of libiomp5md.dll, due to this dll already having
been initialised, presumably by Intel MKL usage from NumPy. The below code is a
workaround suggested by mattslezak-shell to fix
to fix this by setting the KMP_DUPLICATE_LIB_OK environment variable to true.
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'The code should be run at the start of any notebook or program.
The cmdty-storage package should be compatible with the Python interpreter up to version 3.11.
Limitations on the Python version which the cmdty-storage package can be used are largely driven by the pythonnet package dependency. The latest version depends on pythonnet version 3.0.1, which itself works with Python up to version 3.11. Hence this is also the maximum version with which cmdty-storage works.
This section introduces how to use the C# API. See /samples/csharp for a solution containing C# code which can directly be compiled and executed.
In order for storage capacity to be valued, first an instance of the class CmdtyStorage needs to be created. The code samples below shows how the fluent builder API can be used to achieve this. Once the Cmdty.Storage package has been installed, a good way to discover the flexibility in the API is to look at the IntelliSense suggestions in Visual Studio.
The code below shows simple storage facility with constant parameters.
const double constantMaxInjectRate = 5.26;
const double constantMaxWithdrawRate = 14.74;
const double constantMaxInventory = 1100.74;
const double constantMinInventory = 0.0;
const double constantInjectionCost = 0.48;
const double constantWithdrawalCost = 0.74;
CmdtyStorage<Day> storage = CmdtyStorage<Day>.Builder
.WithActiveTimePeriod(new Day(2019, 9, 1), new Day(2019, 10, 1))
.WithConstantInjectWithdrawRange(-constantMaxWithdrawRate, constantMaxInjectRate)
.WithConstantMinInventory(constantMinInventory)
.WithConstantMaxInventory(constantMaxInventory)
.WithPerUnitInjectionCost(constantInjectionCost, injectionDate => injectionDate)
.WithNoCmdtyConsumedOnInject()
.WithPerUnitWithdrawalCost(constantWithdrawalCost, withdrawalDate => withdrawalDate)
.WithNoCmdtyConsumedOnWithdraw()
.WithNoCmdtyInventoryLoss()
.WithNoInventoryCost()
.MustBeEmptyAtEnd()
.Build();The code below shows how to create a more complicated storage object with injection/withdrawal rates being dependent on time and the inventory level.This is much more respresentative of real physical gas storage capacity.
const double constantInjectionCost = 0.48;
const double constantWithdrawalCost = 0.74;
var injectWithdrawConstraints = new List<InjectWithdrawRangeByInventoryAndPeriod<Day>>
{
(period: new Day(2019, 9, 1), injectWithdrawRanges: new List<InjectWithdrawRangeByInventory>
{
(inventory: 0.0, (minInjectWithdrawRate: -44.85, maxInjectWithdrawRate: 56.8)), // Inventory empty, highest injection rate
(inventory: 100.0, (minInjectWithdrawRate: -45.01, maxInjectWithdrawRate: 54.5)),
(inventory: 300.0, (minInjectWithdrawRate: -45.78, maxInjectWithdrawRate: 52.01)),
(inventory: 600.0, (minInjectWithdrawRate: -46.17, maxInjectWithdrawRate: 51.9)),
(inventory: 800.0, (minInjectWithdrawRate: -46.99, maxInjectWithdrawRate: 50.8)),
(inventory: 1000.0, (minInjectWithdrawRate: -47.12, maxInjectWithdrawRate: 50.01)) // Inventory full, highest withdrawal rate
}),
(period: new Day(2019, 9, 20), injectWithdrawRanges: new List<InjectWithdrawRangeByInventory>
{
(inventory: 0.0, (minInjectWithdrawRate: -31.41, maxInjectWithdrawRate: 48.33)), // Inventory empty, highest injection rate
(inventory: 100.0, (minInjectWithdrawRate: -31.85, maxInjectWithdrawRate: 43.05)),
(inventory: 300.0, (minInjectWithdrawRate: -31.68, maxInjectWithdrawRate: 41.22)),
(inventory: 600.0, (minInjectWithdrawRate: -32.78, maxInjectWithdrawRate: 40.08)),
(inventory: 800.0, (minInjectWithdrawRate: -33.05, maxInjectWithdrawRate: 39.74)),
(inventory: 1000.0, (minInjectWithdrawRate: -34.80, maxInjectWithdrawRate: 38.51)) // Inventory full, highest withdrawal rate
})
};
CmdtyStorage<Day> storage = CmdtyStorage<Day>.Builder
.WithActiveTimePeriod(new Day(2019, 9, 1), new Day(2019, 10, 1))
.WithTimeAndInventoryVaryingInjectWithdrawRatesPiecewiseLinear(injectWithdrawConstraints)
.WithPerUnitInjectionCost(constantInjectionCost, injectionDate => injectionDate)
.WithNoCmdtyConsumedOnInject()
.WithPerUnitWithdrawalCost(constantWithdrawalCost, withdrawalDate => withdrawalDate)
.WithNoCmdtyConsumedOnWithdraw()
.WithNoCmdtyInventoryLoss()
.WithNoInventoryCost()
.MustBeEmptyAtEnd()
.Build();The following example shows how to calculate the intrinsic value of the storage, including the optimal intrinsic inject/withdraw decision profile.
var currentPeriod = new Day(2019, 9, 15);
const double lowerForwardPrice = 56.6;
const double forwardSpread = 87.81;
double higherForwardPrice = lowerForwardPrice + forwardSpread;
var forwardCurveBuilder = new TimeSeries<Day, double>.Builder();
foreach (var day in new Day(2019, 9, 15).EnumerateTo(new Day(2019, 9, 22)))
forwardCurveBuilder.Add(day, lowerForwardPrice);
foreach (var day in new Day(2019, 9, 23).EnumerateTo(new Day(2019, 10, 1)))
forwardCurveBuilder.Add(day, higherForwardPrice);
const double startingInventory = 50.0;
IntrinsicStorageValuationResults<Day> valuationResults = IntrinsicStorageValuation<Day>
.ForStorage(storage)
.WithStartingInventory(startingInventory)
.ForCurrentPeriod(currentPeriod)
.WithForwardCurve(forwardCurveBuilder.Build())
.WithCmdtySettlementRule(day => day.First<Month>().Offset(1).First<Day>().Offset(5)) // Commodity is settled on the 5th day of the next month
.WithDiscountFactorFunc((valDate, cfDate) => 1.0) // Assumes no discounting (don't do this in practice)
.WithFixedGridSpacing(10.0)
.WithLinearInventorySpaceInterpolation()
.WithNumericalTolerance(1E-12)
.Calculate();
Console.WriteLine("Calculated intrinsic storage NPV: " + valuationResults.Npv.ToString("N2"));When run, the above code prints the following to the console.
Calculated intrinsic storage NPV: 10,827.21
The code sample below shows how to calculate the optimal storage value, including extrinsic option value, using the Least Squares Monte Carlo valuation technique, and a seasonal 3-factor model of price dynamics.
const double constantInjectionCost = 0.48;
const double constantWithdrawalCost = 0.74;
var injectWithdrawConstraints = new List<InjectWithdrawRangeByInventoryAndPeriod<Day>>
{
(period: new Day(2019, 9, 1), injectWithdrawRanges: new List<InjectWithdrawRangeByInventory>
{
(inventory: 0.0, (minInjectWithdrawRate: -44.85, maxInjectWithdrawRate: 56.8)), // Inventory empty, highest injection rate
(inventory: 100.0, (minInjectWithdrawRate: -45.01, maxInjectWithdrawRate: 54.5)),
(inventory: 300.0, (minInjectWithdrawRate: -45.78, maxInjectWithdrawRate: 52.01)),
(inventory: 600.0, (minInjectWithdrawRate: -46.17, maxInjectWithdrawRate: 51.9)),
(inventory: 800.0, (minInjectWithdrawRate: -46.99, maxInjectWithdrawRate: 50.8)),
(inventory: 1000.0, (minInjectWithdrawRate: -47.12, maxInjectWithdrawRate: 50.01)) // Inventory full, highest withdrawal rate
}),
(period: new Day(2019, 9, 20), injectWithdrawRanges: new List<InjectWithdrawRangeByInventory>
{
(inventory: 0.0, (minInjectWithdrawRate: -31.41, maxInjectWithdrawRate: 48.33)), // Inventory empty, highest injection rate
(inventory: 100.0, (minInjectWithdrawRate: -31.85, maxInjectWithdrawRate: 43.05)),
(inventory: 300.0, (minInjectWithdrawRate: -31.68, maxInjectWithdrawRate: 41.22)),
(inventory: 600.0, (minInjectWithdrawRate: -32.78, maxInjectWithdrawRate: 40.08)),
(inventory: 800.0, (minInjectWithdrawRate: -33.05, maxInjectWithdrawRate: 39.74)),
(inventory: 1000.0, (minInjectWithdrawRate: -34.80, maxInjectWithdrawRate: 38.51)) // Inventory full, highest withdrawal rate
})
};
var storageCapacityStart = new Day(2019, 9, 1);
var storageCapacityEnd = new Day(2019, 10, 1);
CmdtyStorage<Day> storage = CmdtyStorage<Day>.Builder
.WithActiveTimePeriod(storageCapacityStart, storageCapacityEnd)
.WithTimeAndInventoryVaryingInjectWithdrawRatesPiecewiseLinear(injectWithdrawConstraints)
.WithPerUnitInjectionCost(constantInjectionCost, injectionDate => injectionDate)
.WithNoCmdtyConsumedOnInject()
.WithPerUnitWithdrawalCost(constantWithdrawalCost, withdrawalDate => withdrawalDate)
.WithNoCmdtyConsumedOnWithdraw()
.WithNoCmdtyInventoryLoss()
.WithNoInventoryCost()
.MustBeEmptyAtEnd()
.Build();
const double lowerForwardPrice = 56.6;
const double forwardSpread = 87.81;
double higherForwardPrice = lowerForwardPrice + forwardSpread;
var forwardCurveBuilder = new TimeSeries<Day, double>.Builder();
foreach (var day in storageCapacityStart.EnumerateTo(new Day(2019, 9, 22)))
forwardCurveBuilder.Add(day, lowerForwardPrice);
foreach (var day in new Day(2019, 9, 23).EnumerateTo(storageCapacityEnd))
forwardCurveBuilder.Add(day, higherForwardPrice);
const double flatInterestRate = 0.055;
// 3-Factor Seasonal Model Parameters
const double longTermVol = 0.17;
const double seasonalVol = 0.32;
const double spotFactorVol = 0.7;
const double spotFactorMeanReversionRate = 90.6;
MultiFactorParameters<Day> threeFactorParameters = MultiFactorParameters.For3FactorSeasonal(spotFactorMeanReversionRate, spotFactorVol,
longTermVol, seasonalVol, storage.StartPeriod, storage.EndPeriod);
const double startingInventory = 50.0;
const int regressMaxDegree = 3;
const int numInventorySpacePoints = 50;
const int numSims = 500;
const int randomSeed = 11;
var valuationParameters = new LsmcValuationParameters<Day>.Builder
{
BasisFunctions = BasisFunctionsBuilder.Ones +
BasisFunctionsBuilder.AllMarkovFactorAllPositiveIntegerPowersUpTo(regressMaxDegree, 1) + Sim.Spot,
CurrentPeriod = new Day(2019, 8, 29),
DiscountFactors = StorageHelper.CreateAct65ContCompDiscounter(flatInterestRate),
ForwardCurve = forwardCurveBuilder.Build(),
GridCalc = FixedSpacingStateSpaceGridCalc.CreateForFixedNumberOfPointsOnGlobalInventoryRange(storage, numInventorySpacePoints),
Inventory = startingInventory,
Storage = storage,
SettleDateRule = deliveryDate => Month.FromDateTime(deliveryDate.Start).Offset(1).First<Day>() + 19, // Settlement on 20th of following month (business days ignore for simplicity),
SimulationDataReturned = SimulationDataReturned.None
}
.SimulateWithMultiFactorModelAndMersenneTwister(threeFactorParameters, numSims, randomSeed)
.Build();
LsmcStorageValuationResults<Day> results = LsmcStorageValuation.WithNoLogger.Calculate(valuationParameters);
Console.WriteLine("Calculated storage NPV: " + results.Npv.ToString("N2"));The above code prints the following.
Calculated storage NPV: 25,473.10
The code sample below shows how to calculate the optimal storage value, including extrinsic option value, using a one-factor trinomial tree model.
var currentPeriod = new Day(2019, 9, 15);
const double lowerForwardPrice = 56.6;
const double forwardSpread = 87.81;
double higherForwardPrice = lowerForwardPrice + forwardSpread;
var forwardCurveBuilder = new TimeSeries<Day, double>.Builder();
foreach (var day in new Day(2019, 9, 15).EnumerateTo(new Day(2019, 9, 22)))
forwardCurveBuilder.Add(day, lowerForwardPrice);
foreach (var day in new Day(2019, 9, 23).EnumerateTo(new Day(2019, 10, 1)))
forwardCurveBuilder.Add(day, higherForwardPrice);
TimeSeries<Month, Day> cmdtySettlementDates = new TimeSeries<Month, Day>.Builder
{
{new Month(2019, 9), new Day(2019, 10, 20) }
}.Build();
const double interestRate = 0.025;
// Trinomial tree model parameters
const double spotPriceMeanReversion = 5.5;
const double onePeriodTimeStep = 1.0 / 365.0;
TimeSeries<Day, double> spotVolatility = new TimeSeries<Day, double>.Builder
{
{new Day(2019, 9, 15), 0.975},
{new Day(2019, 9, 16), 0.97},
{new Day(2019, 9, 17), 0.96},
{new Day(2019, 9, 18), 0.91},
{new Day(2019, 9, 19), 0.89},
{new Day(2019, 9, 20), 0.895},
{new Day(2019, 9, 21), 0.891},
{new Day(2019, 9, 22), 0.89},
{new Day(2019, 9, 23), 0.875},
{new Day(2019, 9, 24), 0.872},
{new Day(2019, 9, 25), 0.871},
{new Day(2019, 9, 26), 0.870},
{new Day(2019, 9, 27), 0.869},
{new Day(2019, 9, 28), 0.868},
{new Day(2019, 9, 29), 0.867},
{new Day(2019, 9, 30), 0.866},
{new Day(2019, 10, 1), 0.8655}
}.Build();
const double startingInventory = 50.0;
TreeStorageValuationResults<Day> valuationResults = TreeStorageValuation<Day>
.ForStorage(storage)
.WithStartingInventory(startingInventory)
.ForCurrentPeriod(currentPeriod)
.WithForwardCurve(forwardCurveBuilder.Build())
.WithOneFactorTrinomialTree(spotVolatility, spotPriceMeanReversion, onePeriodTimeStep)
.WithMonthlySettlement(cmdtySettlementDates)
.WithAct365ContinuouslyCompoundedInterestRate(settleDate => interestRate)
.WithFixedGridSpacing(10.0)
.WithLinearInventorySpaceInterpolation()
.WithNumericalTolerance(1E-12)
.Calculate();
Console.WriteLine("Calculated storage NPV: " + valuationResults.NetPresentValue.ToString("N2"));The above code prints the following.
Calculated storage NPV: 24,799.09
Add Excel functions included in the add-in have a "cmdty." prefix to their names. All functions can also be seen under the category "Cmdty.Storage" in the Excel function Wizard/
The folder /samples/excel/ of this repo contains some sample spreadsheets which can be used to learn the the functionality.
There are two modes under which the Monte Carlo valuation functions (currently just cmdty.StorageValueThreeFactor) run: async and blocking. The argument Calc_mode of these functions control which mode is used for execution.
Blocking mode results in the valuation functions behaving like typical Excel functions, with all other operations in Excel freezing until the functions return. If calculating functions with blocking mode then the Excel application calculation mode should be set to manual, otherwise each time any input is changed the storagevaluation calculations will be kicked off, freezing Excel.
Under async mode the Excel function returns immediately upon calculation and the cell is set to Pending status. When the valuation is started, using one of the methods described below, the calculation is performed in the background with no freezing of the Excel UI during calculation. Excel-DNA RTD functionality is used to notify Excel when calculations are complete with the resulting figures pushed to the UI.
Generally async mode gives a more slick user experience with the following additional characteristics:
- Progress updates via the cmdty.SubscribeProgress function. Progress bars in Excel cells can be achieved using conditional formatting data bars.
- Cancellation of storage calculations before they have completed.
- Parallel valuation of multiple storage facilities.
It is recommended to have the Excel application calculation mode set to Automatic if using async mode, otherwise you will have to keep press F9 (calculate) to check progress and whether the completed status.
Buttons in the Cmdty.Storage Excel ribbon can be used to calculate all pending, cancel all
running, and reset all cancelled calculations in the current Excel instance.
Similarly Excel context menu items can be used to perform the same operations, but specifically
on the selected cells.
The Excel functions cmdty.StartPending, cmdty.CancelRunning and cmdty.ResetCancelled can be used to perform the same operations. These were included with the intention to be called from VBA. COM wrapped C# classes, the conventional approach to provide VBA-callable code, was not used due to complications experienced during development.
An example of async calculation mode can be see in /samples/excel/three_factor_storage.xlsm .
See this page for some ideas about calibration. Example Python code for calibrating the 3-factor seasonal model will be added to this repo later.
This section describes how to run a scripted build on a cloned repo. Visual Studio 2022 is used for development, and can also be used to build the C# and run unit tests on the C# and Python APIs. However, the scripted build process also creates packages (NuGet and Python), builds the C# samples, and verifies the C# interactive documentation. Cake is used for running scripted builds. The ability to run a full scripted build on non-Windows is planned, but at the moment it can only be done on Windows.
The following are required on the host machine in order for the build to run.
- The .NET Core SDK. Check the global.json file for the version necessary, taking into account the matching rules used.
- The Python interpretter, accessible by being in a file location in the PATH environment variable. See Python Version Compatibility for details of which Python version should work.
- The following Python packages installed:
- virtualenv.
- setuptools.
- wheel.
The build is started by running the PowerShell script build.ps1 from a PowerShell console, ISE, or the Visual Studio Package Manager Console.
PM> .\build.ps1
The following results of the build will be saved into the artifacts directory (which itelf will be created in the top directory of the repo).
- The NuGet package: Cmdty.Storage.[version].nupkg
- The Python package files:
- cmdty_storage-[version]-py3-none-any.whl
- 32-bit and 64-bit versions of the Excel add-in:
- Cmdty.Storage-x86.zip
- Cmdty.Storage-x64.zip
At the moment only building, testing and packaging the .NET components is possible on a non-Windows OS.
The following are required on the host machine in order for the build to run.
- The .NET Core SDK. Check the global.json file for the version necessary, taking into account the matching rules used.
Run the following commands in a cloned repo
> dotnet build src/Cmdty.Storage/ -c Release
> dotnet test tests/Cmdty.Storage.Test/ -c Release
> dotnet pack src/Cmdty.Storage -o artifacts -c Release --no-build
The following results of the build will be saved into the artifacts directory (which itelf will be created in the top directory of the repo).
- The NuGet package: Cmdty.Storage.[version].nupkg
Users of the Python API might be perplexed as to the technology used: Python calling into .NET, which itself calls into native code for the Intel MKL numerical routines. This is certainly not a common structure, especially for a package focussed on complex numerical calculations. Where the Python runtime speed is an issue, as is suspected with this project, it is more usual to have a structure where Python calls into native code using ctypes, or makes use of a Numba.
However, the Cmdty project started off as a .NET only project, written in C#, due to the author being mainly a C# guy during the day-job. The Python wrapper was added later as it became apparent that there was a demand to use the models from Python. Since then it now seems that there are many more users of the Python API than the C# NuGet package, resulting in significant time being spent on the Python API, and examples.
If the project was started again from scratch, potentially it would have been written entirely in Python utilising Numba. However, due to time constraints, and not wanting to have the maintenance headache of having two independent implementations side-by-side there is no plan to work on this. That said, if others want to have a go at a pure Python implementation it would be very much welcomed and I would happily help out.
Despite the oddity in the structure it seems to work quite well with the performance of the LSMC model being decent. Although compiled C# usually does not run as quickly as native code, it's performance isn't bad at all, and the majority of the running time is spent during the QR decomposition for the regression which is itself done using Intel MKL, which does these calculations pretty much as quickly as you can get. The only real annoyances with the structure is:
- pythonnet not currently supporting .NET Core. A fix for this is currently in the pipeline for the next pythonnet release. At current this means that Mono needs to be installed in order to use the Python API on Linux.
- The PyPI package size.
- If a version of the curves package is installed which has a .NET dependency with a different version to a dependency of the cmdty-storage package this can cause strange errors.
This section contains the procedure to follow in order to debug the calculations in the C# code, as invoked from Python running in a Jupyter notebook. The following steps are a prerequisite to the procedures described below.
- Install the following software for building the C#:
- Visual Studio 2022.
- The .NET Core SDK version, as described in the section Build Prerequisites.
- In Visual Studio uncheck the box for the debugging option "Enable Just My Code" in Tools > Options > Debugging > General.
- Clone the storage repo onto your machine.
The below descriptions have been used from a Windows desktop. As Visual Studio is available for Apple computers a similar procedure might work with Apple hardware, but has never been tested.
This section describes how to debug the execution of the cmdty-storage package installed from PyPI.
- Do a git checkout to the git tag associated with the version of the cmdty-storage package you are running in Jupyter. The git tags for each release are found on GitHub here.
- In the cloned repo open Cmdty.Storage.sln in Visual Studio and build in Debug configuration.
- Set breakpoints in the C# code. To investigate the Least Squares Monte Carlo valuation, a good place for a breakpoin is at the start of the Calculate method of the class LsmcStorageValuation, as found in LsmcStorageValuation.cs.
- It is likely that there are many running processes for the python.exe interpretter. It is
necessary to identify the PID (Process ID) of the exact python.exe process which is being used
by Jupyter. One way to do this uses Sysinternals Process Explorer:
- Launch Process Explorer and ensure PID is one of the displayed columns.
- Order the displayed processes by process name and locate the section which contains the python.exe processes.
- Run the Jupyter notebook and observe the specific python.exe process for which the CPU usage
increases, taking a note of the PID. In the image below the PID is found to be 33568.
- In the Visual Studio menu bar select Debug > Attach to Process. In the resulting dialogue box search the processes using the noted PID. Select this process and press the Attach button.
- Execute the Jupyter notebook. The C# code should break at the placed breakpoints.
This section describes the more advanced scenario of running and debugging Cmdty.Storage code which has been modified, and so is different to that used to created released PyPI packages. The process of debugging the C# code with custom modifications is identical to that described above, except that a pip local project install is required. This should be done in the Anaconda Prompt using the path of the directory src\Cmdty.Storage.Python\ within the cloned cmdty-storage repo as the path in the pip install command.
It's always motivating to hear how the model is being used, especially by practitioners in the energy trading sector. Much progress on the LSMC model would not have been possible without the input and motivation provided by collaborators from industry. So don't hesitate to get in touch to discuss storage modelling or suggest future enhancements. Also, show your appreciation by giving this repo a star!
This project is licensed under the MIT License - see the LICENSE file for details.
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