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Let's Buildīefore getting started, you will need to create an Alpaca account to use the paper trading as well as fetch market data for ETH/USD. In such cases, checks 3 and 4 help limit our losses. We might also encounter scenarios where the current price of the asset never reaches the limit price of our order. Ideally, we would like only checks 1 and 2 to execute alternatively (buy and sell).

If we do have a position, have a sell order in place for the asset but if the current price of the asset is 0.5% below the buying price of the asset, we cancel our sell limit order and close our position.

This indicates that the market is trending upwards and we need to recalibrate our buying and selling prices.

If we do not have any position on the asset we are looking to trade, have a buy order in place for the asset but if the current price of the asset is above 0.5% of our estimated price we would have sold our asset at, then we cancel our existing limit buy order.

In code, this price is referenced as selling_price.

The above step is immediately followed by placing a limit sell order at a price 0.2% below the maximum price of the asset over the last 10 minutes.

In code, this price is referenced as buying_price.

If the current price of the asset is above the minimum price over the last 10 minutes, place a buy limit order at a price 0.2% above the minimum price.We will use the following logic to place our trades: The bot will use data over the last 10 minutes to decide the buying and selling prices for our limit orders. We are building an algorithmic trading bot that will try to capture small profits trading ETH/USD but do it quite often (a few times within an hour hopefully). This is where having an algorithmic trading strategy can help keep a control on the prices we trade at. To execute this strategy successfully, a trader needs to have a tight control on their entry and exits. The key idea is that it is easier to capture a small percentage change in the price of an asset than to predict a larger move.

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