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https://github.com/alpacahq/example-scalping

A working example algorithm for scalping strategy trading multiple stocks concurrently using python asyncio
https://github.com/alpacahq/example-scalping

algorithmic-trading algotrading alpaca api async asyncio fintech hft-trading market-data python3 scalping trading-bot trading-strategy

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A working example algorithm for scalping strategy trading multiple stocks concurrently using python asyncio

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README 

        
# Concurrent Scalping Algo



This python script is a working example to execute 

[scalping trading](https://www.investopedia.com/articles/trading/05/scalping.asp) 

algorithm for [Alpaca API](https://alpaca.markets). This algorithm uses real time order updates

as well as minute level bar streaming from Polygon via Websockets (see the

[document](https://docs.alpaca.markets/market-data/#consolidated-market-data) for

Polygon data access).

One of the contributions of this example is to demonstrate how to handle

multiple stocks concurrently as independent routine using Python's

[asyncio](https://docs.python.org/3/library/asyncio.html).



The strategy holds positions for very short period and exits positions quickly, so

you have to have more than $25k equity in your account due to the Pattern Day Trader rule,

to run this example. For more information about PDT rule, please read the

[document](https://support.alpaca.markets/hc/en-us/articles/360012203032-Pattern-Day-Trader).



## Dependency

This script needs latest [Alpaca Python SDK](https://github.com/alpacahq/alpaca-trade-api-python).

Please install it using pip



```sh

$ pip3 install alpaca-trade-api

```



or use [pipenv](https://github.com/pypa/pipenv) using `Pipfile` in this directory.



```sh

$ pipenv install

```



## Usage



```sh

$ python main.py --lot=2000 TSLA FB AAPL

```



You can specify as many symbols as you want.  The script is designed to kick off while market

is open. Nothing would happen until 21 minutes from the market open as it relies on the

simple moving average as the buy signal.



## Strategy

The algorithm idea is to buy the stock upon the buy signal (20 minute

[moving average crossover](https://www.investopedia.com/articles/active-trading/052014/how-use-moving-average-buy-stocks.asp)) 

as much as `lot` amount of dollar, then immediately sell the position at or above the entry price.

The assumption is that the market is bouncing upward when this signal occurs in a short period of time.

The buy signal is extremely simple, but what this strategy achieves is the quick reaction to

exit the position as soon as the buy order fills. There are reasonable probabilities that you can sell

the positions at the better prices than or the same price as your entry within the small window. We send

limit order at the last trade or position entry price whichever the higher to avoid unnecessary slippage.



The buy order is canceled after 2 minutes if it does not fill, assuming the signal is not

effective anymore. This could happen in a fast-moving market situation. The sell order is left

indifinitely until it fills, but this may cause loss more than the accumulated profit depending

on the market situation. This is where you can improve the risk control beyond this example.



The buy signal is calculated as soon as a minute bar arrives, which typically happen about 4 seconds

after the top of every minute (this is Polygon's behavior for minute bar streaming).



This example liquidates all watching positions with market order at the end of market hours (03:55pm ET).



## Implementation

This example heavily relies on Python's asyncio. Although the thread is single, we handle

multiple symbols concurrently using this async loop.



We keep track of each symbol state in a separate `ScalpAlgo` class instance. That way,

everything stays simple without complex data structure and easy to read. The `main()`

function creates the algo instance for each symbol and creates streaming object

to listen the bar events. As soon as we receive a minute bar, we invoke event handler

for each symbol.



The main routine also starts a period check routine to do some work in background every 30 seconds.

In this background task, we check market state with the clock API and liquidate positions

before the market closes.



### Algo Instance and State Management

Each algo instance initializes its state by fetching day's bar data so far and position/order

from Alpaca API to synchronize, in case the script restarts after some trades. There are

four internal states and transitions as events happen.



- `TO_BUY`: no position, no order. Can transition to `BUY_SUBMITTED`

- `BUY_SUBMITTED`: buy order has been submitted. Can transition to `TO_BUY` or `TO_SELL`

- `TO_SELL`: buy is filled and holding position. Can transition to `SELL_SUBMITTED`

- `SELL_SUBMITTED`: sell order has been submitted. Can transition to `TO_SELL` or `TO_BUY`



### Event Handlers

`on_bar()` is an event handler for the bar data. Here we calculate signal that triggers

a buy order in the `TO_BUY` state. Once order is submitted, it goes to the `BUY_SUBMITTED`

state.



If order is filled, `on_order_update()` handler is called with `event=fill`. The state

transitions to `TO_SELL` and immediately submits a sell order, to transition to the

`SELL_SUBMITTED` state.



Orders may be canceled or rejected (caused by this script or you manually cancel them

from the dashboard). In these cases, the state transitions to `TO_BUY` (if not holding

a position) or `TO_SELL` (if holding a position) and wait for the next events.



`checkup()` method is the background periodic job to check several conditions, where

we cancel open orders and sends market sell order if there is an open position.



It exits once the market closes.



### Note

Each algo instance owns its child logger, prefixed by the symbol name. The console

log is also emitted to a file `console.log` under the same directory for your later review.



Again, the beautify of this code is that there is no multithread code but each

algo instance can focus on the bar/order/position data only for its own. It still

handles multiple symbols concurrently plus runs background periodic job in the

same async loop.



The trick to run additional async routine is as follows.



```py

    loop = stream.loop

    loop.run_until_complete(asyncio.gather(

        stream.subscribe(channels),

        periodic(),

    ))

    loop.close()

```



We use `asyncio.gather()` to run all bar handler, order update handler and periodic job

in one async loop indifinitely. You can kill it by `Ctrl+C`.



### Customization

Instead of using this buy signal of 20 minute simple moving average cross over, you can

use your own buy signal. To do so, extend the `ScalpAlgo` class and write your own

`_calc_buy_signal()` method.



```py

    class MyScalpAlgo(ScalpAlgo):

        def _calculate_buy_signal(self):

            '''self._bars has all minute bars in the session so far. Return True to

            trigger buy order'''

            pass

```



And use it instead of the original class.