Getting started¶

Preface¶

To get started you need

Basic algorithmic trade, finance and quantitave research understanding
Basic Python understanding
Basic Jupyter Notebook understanding
An environment to run Jupyter Notebooks: Visual Studio Code, Jupyter server or your local computer or a cloud service like Binder

If you are new to these topics please see the Learn section for useful links to start learning more.

API keys¶

When you run the notebook for the first time, you are asked to register an API key for Trading Strategy dataset download. You can also sign up for the API key on the website.

Quickstart¶

Trading Strategy offers running its notebooks for free on Binder, a cloud Jupyter notebook hosting service.

Clicking the Launch binder button on any notebook in this documentation will automatically direct you to a runnable server on Binder service
This document is displaying the results of the last notebook run. Running the notebook yourself will execute all the cells and update the result of calculations

About this Getting started example¶

This notebook you are reading is “Hello World” where we make our first dynamic market analysis.

The code belows creates a Trading Strategy API client instance that is used to communicate with the dataset server.

If you do not have an API key saved on the Notebook server yet, this will prompt you to create an API key and then saves it for you. Please answer the interactive questions when the notebook is running.

[14]:

from tradingstrategy.client import Client

client = Client.create_jupyter_client()

Started Trading Strategy in Jupyter notebook environment, configuration is stored in /Users/moo/.tradingstrategy

Now when the API key is created and we are connected, we can do some analysis. Let’s get the top 10 supported exchanges by their 30 days trading volume for supported tokens.

[15]:

# Let's log the date when the this notebook was run,
# as the notebook and its output will be on the website documentation
import datetime
print(f"This Jupyter notebook was run and the results captured at {datetime.date.today()}")

This Jupyter notebook was run and the results captured at 2022-01-10

Then let’s extract some montly statistics from the exchange dataset.

[16]:

from typing import List
from IPython.display import display
import pandas as pd
import numpy as np
from tradingstrategy.chain import ChainId
from tradingstrategy.exchange import Exchange

universe = client.fetch_exchange_universe()

# Have nice type decoration to make the tutorial
# more readable
exchanges: List[Exchange] = []
volumes: List[float] = []
chains: List[str] = []

for xchg in universe.get_top_exchanges_by_30d_volume()[0:10]:
    exchanges.append(xchg.name)
    # Format volume with the  thousand separator
    volumes.append("{:,.2f}".format(xchg.vol_30d))
    # Pull blockchain name for the chain id number from
    # chain data database
    chain_id: ChainId = xchg.chain_id
    chains.append(chain_id.get_name())

# Convert output to Pandas DataFrame object so that
# notebook can render it as a nice table
df = pd.DataFrame({"Exchange": exchanges, "Blockchain": chains, "USD Volume 30d": volumes})

# Index rows starting with one instead of zero
df.index = np.arange(1, len(df)+1)

# Show dataframe as HTML table
display(df)

	Exchange	Blockchain	USD Volume 30d
1	PancakeSwap v2	Binance Smart Chain	22,570,802,493.35
2	Uniswap v2	Ethereum	10,187,449,749.76
3	Sushiswap	Ethereum	8,535,474,344.84
4	Biswap	Binance Smart Chain	5,534,392,623.57
5	BabySwap	Binance Smart Chain	3,730,382,286.98
6	Quickswap	Polygon	1,628,781,813.87
7	GIBX Swap	Binance Smart Chain	848,028,627.98
8	ApeSwap	Binance Smart Chain	680,577,965.38
9	ErosSwap	Binance Smart Chain	658,118,532.04
10	SushiSwap	Polygon	629,315,682.02

Ta-da - all done! Now you can proceed to view more complex examples.