Learn / Track V / lesson 5 of 5
Grid and DCA strategies, honestly
Two of the most popular retail bot strategies — what they are really short or long, and how to stop them from ruining you.
Grid bots and dollar-cost averaging are everywhere in crypto, and the vault has dozens of each. Both are reasonable tools. Both are routinely misunderstood.
How a grid works
Place buy orders at evenly spaced levels below the current price and sell orders above. Each time price drops through a level, you buy; each time it rises through the next level, you sell that unit for one grid step of profit. In a choppy, range-bound market, the grid harvests oscillation over and over.
With levels spaced apart, each completed round trip earns about per unit, minus two sets of fees. Grid density is a trade-off: tighter grids trade more often but earn less per trade and pay more in fees.
What a grid is really betting on
A grid is a short-volatility, mean-reversion strategy. It profits when price oscillates within the range and suffers when price trends away:
- Trend down: the grid keeps buying all the way down and ends up holding a full inventory of a falling asset. The realised “grid profits” are small next to the unrealised loss.
- Trend up: the grid sells out early and misses the move. Less painful, but a real opportunity cost.
This payoff is similar to being short an option straddle: steady income in quiet markets, losses in big moves. Read the lesson on mean reversion for the general risk profile.
Making a grid survivable
- Hard inventory cap — never more than a fixed fraction of capital in the asset.
- Range-break rule — if price closes outside the grid by more than some margin, stop adding and, ideally, cut.
- Volatility-aware spacing — set the grid step as a multiple of ATR rather than a fixed percentage.
- Fee-aware spacing — each step must comfortably exceed round-trip fees plus expected slippage.
Dollar-cost averaging
DCA buys a fixed amount on a schedule regardless of price. Because a fixed amount buys more units when price is low, your average cost per unit is the harmonic mean of the prices, which is always at or below the arithmetic mean.
That property is real but often oversold. If the asset’s expected return is positive, investing a lump sum immediately has historically beaten DCA most of the time, simply because the money is invested for longer. DCA’s genuine benefits are behavioural and practical: it reduces regret, matches investing to income, and limits the damage of buying at a single bad moment.
“DCA” bots that buy more after each drop — doubling or scaling the order size — are not DCA. They are martingale, and they share its fate: eventually a drawdown arrives that exhausts the capital before the bounce. Read the Kelly criterion on why sizing up after losses is backwards.
Put it to work
Build this idea
A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.
/agenkit build a grid trading bot for perps from a vault spec with a hard inventory cap, a range-break kill switch, order deduplication, and paper trading by default- npx agenkit install engineering-kit in your project
- Paste the command into your agent and approve each phase
- Get tests, backtest, risk controls and a paper-trading switch