dca-optimizer

This project uses Ktor and Kotlin Language, and it's intended to be just a playground project.

NOTE: This project is not a trading-bot nor a financial adviser tool, try it out on your own.

Dollar-cost averaging and optimization

Dollar-Cost Averaging is an investment strategy where a person constantly buy more of its assets, (every month, or week, etc), in order to avoid trying to time the market.

From Investopedia:

Dollar-cost averaging (DCA) is an investment strategy in which an investor divides up the total amount to be invested across periodic purchases of a target asset in an effort to reduce the impact of volatility on the overall purchase. The purchases occur regardless of the asset's price and at regular intervals.

How optimize the DCA strategy and better balance your portfolio

If you hold multiple assets, you probably should give a weight target for each on them on your portfolio.

The dca-optimizer tries to use some criteria to better distribute your dca investment calculation.

Some criteria are:

• The asset weight has to be smaller than its target.
• How far below the weight is from its target.
• The asset "distance" from its ATH (all-time high) or 52 weeks high how also often used for stocks, can be used to define if an asset will be invested.

Example Scenario

Imagine a hypothetical portfolio with 5 assets:

Ticker	Weight	Target	From ATH
A	25.0%	20.0%	18.0%
B	15.0%	20.0%	8.0%
C	15.0%	25.0%	15.5%
D	10.0%	25.0%	17.1%
F	5.0%	10.0%	22.0%

In a configuration where you define that the ATH threshold is 10% (only assets that are below this value will be invested):

• The first asset with ticker A, won't be invested, because it's over its target.
• The second asset with ticker B, won't be invested, because it's below the ATH threshold of 10%.

The point is to help balance out a portfolio with under/over weighted assets and minimize just a little buying assets that are very high in price currently. (against its ATH/52 weeks price)

This doesn't guarantee any significant portfolio performance on the long term, but it might do slightly better overall.

Strategies

• WEIGHT: The current asset's weight distance from its target is used to determine the DCA distribution. (Assets with same target might get different results)
• TARGET: The asset's target is used to determine the DCA distribution, over-weighted assets are discarded. (Assets with same target will get the same result)
• PORTFOLIO: All assets will be invested, but over-weighted assets will have its target reduced and the difference is distributed among all under-target assets.
• RATING: All rated assets will be invested, ONLY the rating values will be used to calculate the distribution. (Think on a 5 stars rating system)

Payload examples

POST http://localhost:8080/api/optimize

{
    "amount": "1000.00",
    "strategy": {
        "type": "WEIGHT",
        "thresholds": {
            "fromAth": 10.0,
            "overTarget": 0.1
        }
    },
    "assets": [
        {
            "ticker": "A",
            "weight": 25.0,
            "target": 20.0,
            "fromAth": 18.0
        },
        {
            "ticker": "B",
            "weight": 15.0,
            "target": 20.0,
            "fromAth": 8.0
        },
        {
            "ticker": "C",
            "weight": 15.0,
            "target": 25.0,
            "fromAth": 15.5
        },
        {
            "ticker": "D",
            "weight": 10.0,
            "target": 25.0,
            "fromAth": 17.1
        },
        {
            "ticker": "E",
            "weight": 5.0,
            "target": 10.0,
            "fromAth": 22.0
        }
    ]
}

{
    "amount": "1000.00",
    "strategy": {
        "type": "RATING"
    },
    "assets": [
        {
            "ticker": "A",
            "rating": 3
        },
        {
            "ticker": "B",
            "rating": 5
        },
        {
            "ticker": "C",
            "rating": 5
        },
        {
            "ticker": "D",
            "fromAth": 2
        },
        {
            "ticker": "E",
            "rating": 4
        }
    ]
}

TODOs

• Add instructions to run the application locally or using a docker image.
• Add a configuration option to consider the ATH distance in the calculations.
• Payload validations.
• Make it available via docker image.
• Fetch data from online sources to calculate the ATH percentage for actual asset tickers (Stocks, ETFs, Cryptos).