Dollar Cost Averaging
Background
Dollar cost averaging (DCA) is an investment strategy that involves regularly investing a fixed amount of money into a particular asset regardless of the asset's price. This means purchasing more shares when prices are low and fewer shares when prices are high. The main objective is to reduce the impact of volatility on the overall purchase and avoid the pitfalls of trying to time the market.
Dollar Cost Averaging Strategy
Regardless of the price of token y, we swap self.buying_amount
number of token x for token y. We do this trade every self.min_dist
block
if we have enough token x (more than 0) in the agent's portfolio.
How To Run
Installation
Follow our Getting Started guide to install the dojo library and other required tools.
Then clone the dojo_examples
repository and go into the relevant directory.
Running
Download the dashboard to view the simulation results.
To view example simulation data, download results.db
from the repository and click 'Add A Simulation' on the dashboard.
To run the simulation yourself, use the following command.
This command will setup your local blockchain, contracts, accounts and agents. You can then access your results on your Dojo dashboard by connecting to a running simulation.
Step-By-Step Explanation
Initialization
We create a subclass of BasePolicy
called DCAPolicy
which takes in 2 parameters: buying_amount
and min_dist
.
buying_amount
specifies the amount of tokens we should trade for the other tokenmin_dist
specifies the time interval in blocks between each trade.
On the Ethereum blockchain, the block time is approximately 12 seconds, while the block time on Arbitrum is ~0.26 seconds.
Signal Calculation
Signals allow us to easily view data on our Dojo dashboard. In this example, we are adding a signal to monitor the difference in wealth if the agent bought the token all at once at current price vs. dollar cost averaging.
Trade Execution
Here, we check if we have sufficient funds to purchase tokens and if enough time
has passed since our last trade. If both conditions are met, we set the last_trade_block
to the current block and return a UniswapV3Trade
object specifying the buying amount.
Otherwise, we return an empty list which means do nothing.
In the run.py
file, we create a pool, a Uniswap environment and an agent that implements the dollar cost averaging policy.
Results
You can download the results to this example below.
We offer a dashboard desktop application for visualizing your simulation results. You can download the file for the desktop application here, or just open the results in our hosted dashboard.