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https://github.com/maxbbraun/trump2cash

A stock trading bot powered by Trump tweets
https://github.com/maxbbraun/trump2cash

bot trading trump twitter

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A stock trading bot powered by Trump tweets

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README 

        
# Trump2Cash



This bot watches [Donald Trump's tweets](https://twitter.com/realDonaldTrump)

and waits for him to mention any publicly traded companies. When he does, it

uses sentiment analysis to determine whether his opinions are positive or

negative toward those companies. The bot then automatically executes trades on

the relevant stocks according to the expected market reaction. It also tweets

out a summary of its findings in real time at

[@Trump2Cash](https://twitter.com/Trump2Cash).



*You can read more about the background story [here](https://trump2cash.biz).*



[![Trump2Cash](https://cdn-images-1.medium.com/max/1400/1*VbnhlLnZz0KvWO0QsM5Ihw.png)](https://trump2cash.biz)



The code is written in Python and is meant to run on a

[Google Compute Engine](https://cloud.google.com/compute/) instance. It uses the

[Twitter Streaming APIs](https://dev.twitter.com/streaming/overview) to get

notified whenever Trump tweets. The entity detection and sentiment analysis is

done using Google's

[Cloud Natural Language API](https://cloud.google.com/natural-language/) and the

[Wikidata Query Service](https://query.wikidata.org/) provides the company data.

The [TradeKing API](https://developers.tradeking.com/) does the stock trading.



The [`main`](main.py) module defines a callback where incoming tweets are

handled and starts streaming Trump's feed:



```python

def twitter_callback(tweet):

    companies = analysis.find_companies(tweet)

    if companies:

        trading.make_trades(companies)

        twitter.tweet(companies, tweet)



if __name__ == '__main__':

    twitter.start_streaming(twitter_callback)

```



The core algorithms are implemented in the [`analysis`](analysis.py) and

[`trading`](trading.py) modules. The former finds mentions of companies in the

text of the tweet, figures out what their ticker symbol is, and assigns a

sentiment score to them. The latter chooses a trading strategy, which is either

buy now and sell at close or sell short now and buy to cover at close. The

[`twitter`](twitter.py) module deals with streaming and tweeting out the

summary.



Follow these steps to run the code yourself:



### 1. Create VM instance



Check out the [quickstart](https://cloud.google.com/compute/docs/quickstart-linux)

to create a Cloud Platform project and a Linux VM instance with Compute Engine,

then SSH into it for the steps below. Pick a predefined

[machine type](https://cloud.google.com/compute/docs/machine-types) matching

your preferred price and performance.



#### Container



Alternatively, you can use the [`Dockerfile`](Dockerfile) to build a

[Docker container](https://www.docker.com/what-container) and

[run it on Compute Engine](https://cloud.google.com/compute/docs/containers/deploying-containers)

or other platforms.



```shell

docker build -t trump2cash .

docker tag trump2cash gcr.io//trump2cash

docker push gcr.io//trump2cash:latest

```



### 2. Set up auth



The authentication keys for the different APIs are read from shell environment

variables. Each service has different steps to obtain them.



#### Twitter



Log in to your [Twitter](https://twitter.com/) account and

[create a new application](https://apps.twitter.com/app/new). Under the *Keys

and Access Tokens* tab for [your app](https://apps.twitter.com/) you'll find

the *Consumer Key* and *Consumer Secret*. Export both to environment variables:



```shell

export TWITTER_CONSUMER_KEY=""

export TWITTER_CONSUMER_SECRET=""

```



If you want the tweets to come from the same account that owns the application,

simply use the *Access Token* and *Access Token Secret* on the same page. If

you want to tweet from a different account, follow the

[steps to obtain an access token](https://dev.twitter.com/oauth/overview). Then

export both to environment variables:



```shell

export TWITTER_ACCESS_TOKEN=""

export TWITTER_ACCESS_TOKEN_SECRET=""

```



#### Google



Follow the

[Google Application Default Credentials instructions](https://developers.google.com/identity/protocols/application-default-credentials#howtheywork)

to create, download, and export a service account key.



```shell

export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json"

```



You also need to [enable the Cloud Natural Language API](https://cloud.google.com/natural-language/docs/getting-started#set_up_your_project)

for your Google Cloud Platform project.



#### TradeKing



Log in to your [TradeKing](https://www.tradeking.com/) account and

[create a new application](https://developers.tradeking.com/applications/CreateApplication).

Behind the *Details* button for

[your application](https://developers.tradeking.com/Applications) you'll find

the *Consumer Key*, *Consumer Secret*, *OAuth (Access) Token*, and *Oauth (Access)

Token Secret*. Export them all to environment variables:



```shell

export TRADEKING_CONSUMER_KEY=""

export TRADEKING_CONSUMER_SECRET=""

export TRADEKING_ACCESS_TOKEN=""

export TRADEKING_ACCESS_TOKEN_SECRET=""

```



Also export your TradeKing account number, which you'll find under

*[My Accounts](https://investor.tradeking.com/Modules/Dashboard/dashboard.php)*:



```shell

export TRADEKING_ACCOUNT_NUMBER=""

```



### 3. Install dependencies



There are a few library dependencies, which you can install using

[pip](https://pip.pypa.io/en/stable/quickstart/):



```shell

pip install -r requirements.txt

```



### 4. Run the tests



Verify that everything is working as intended by running the tests with

[pytest](https://doc.pytest.org/en/latest/getting-started.html) using this

command:



```shell

export USE_REAL_MONEY=NO && pytest *.py -vv

```



### 5. Run the benchmark



The [benchmark report](benchmark.md) shows how the current implementation of the

analysis and trading algorithms would have performed against historical data.

You can run it again to benchmark any changes you may have made. You'll need a [Polygon](https://polygon.io) account:



```shell

export POLYGON_API_KEY=""

python benchmark.py > benchmark.md

```



### 6. Start the bot



Enable real orders that use your money:



```shell

export USE_REAL_MONEY=YES

```



Have the code start running in the background with this command:



```shell

nohup python main.py &

```