Freetrade

Now supporting authentication and sessions with the API.

See code below.
python
from freetrade import FreeTrade

email = 'myemail'
ft = FreeTrade(email)

tickers = ft.get_tickers()
postcodes = ft.api.get_address_by_postcode('E15JL')


Next step is retrieving data from the session.

Added a function which loads the downloaded historical data into a `pandas.DataFrame`:
python
df = ft.load_historical_data_as_dataframe()
print(df.tail(), df.shape)


**Note: the date index is not sorted.**

Sample output. Data contains adjusted `closing prices`, rows index are `dates`, column names are `asset ticker symbols`

text
CSCO SVT SDRY BA ... UAA BLK WTB NXT
Date ...
2018-12-25 NaN 1817.5 426.0 NaN ... NaN NaN 4520.0 4014.0
2019-01-01 NaN 1815.5 467.8 NaN ... NaN NaN 4579.0 3991.0
2019-01-21 NaN 1934.5 523.5 NaN ... NaN NaN 4855.0 4726.0
2019-02-18 NaN 1981.5 513.5 NaN ... NaN NaN 4956.0 4858.0
2019-04-19 NaN 1959.0 455.2 NaN ... NaN NaN 4842.0 5774.0

[5 rows x 341 columns] (1305, 341)


Sorting the index
This is not sorted by ascending date. In order to sort it, need to use `sort_index` function from `DataFrame`.
python
df = ft.load_historical_data_as_dataframe()
df.sort_index(inplace=True)
print(df.tail())


text
CSCO SVT SDRY BA ... UAA BLK WTB NXT
Date ...

Created functions:
* `ft.get_ticker_history(ftexchange, ticker, duration)` to get the historical data for any asset.
* `ft.update_historical_prices()` to save the historical data for all assets. If we have older data, we just update it with newer data.

Notes:
* Freetrade uses Quandl for fetching LSE data.

This package is able to get list of tradable assets on Freetrade. It can also generate a list of ticker symbols for Trading View.