This is the companion code for the paper *Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
- by Jay Cao, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang and Jun Yuan. The paper is available here at SSRN.
Gamma & Vega Hedging Codebase
│ run.py - Run D4PG model
│ greek_run.py - Run baseline models
| simulate_env.py - Simulate the environment
└───agent
│ │ agent.py - D4PG agent
│ │ distributional.py - distributional dependency for D4PG
│ │ learning.py - learning module for D4PG
└───environment
│ │ Environment.py - Trading Environment
│ │ Trading.py - Portfolio constructions
│ │ utils.py - Stochastic Processes generation and other utility functions
└───Result Analysis
│ RL&Baseline Result Analysis - Sample RL and Baseline model result analysis
└───Logs
| Sample Log Files - This is NOT the results in the paper! Just a sample log file with 100 train_simulations and 100 evaluation epochs.
dm-env==1.5
gym==0.24.1
numpy==1.23.1
pytest==6.2.5
reverb==2.0.1
scipy==1.8.1
torch==1.11.0
tqdm==4.64.0
dm-acme[jax,tensorflow,envs]==0.4.0
dm-sonnet==2.0.0
dm-launchpad==0.5.0
trfl==1.2.0
pyyaml==5.4.1
xmanager==0.2.0
python run.py -spread=0.005 -obj_func=meanstd -train_sim=40000 -eval_sim=5000 -critic=qr -std_coef=1.645 -init_vol=0.3 -mu=0.0 -vov=0.0 -vega_obs=False -gbm=True -hed_ttm=30 -liab_ttms=60 -init_ttm=30 -poisson_rate=1.0 -action_space=0,1 -logger_prefix=batch1/Table1/TC05/RL/mean_std -n_step=5python greek_run.py -spread=0.02 -gbm=True -eval_sim=5000 -strategy=gamma -init_vol=0.3 -mu=0.0 -vov=0.3 -hed_ttm=30 -liab_ttms=60 -init_ttm=30 -poisson_rate=1.0 -vega_obs=False -logger_prefix=batch1/Table2/TC20/Baseline/vegaTrained and Tested Logs are stored in the Logs folder.
- The implementation of D4PG agent is taken from ACME D4PG.
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