thirtysevena

37AUSTEN's Proprietary Algorithms Library.

These details have not been verified by PyPI

Project links

Homepage

Project description

This is a library that provides access to 37AUSTEN"s proprietary algorithms.

Installation


pip install thirtysevena

Full API Documentation

The full API documentation can be found via https://www.37austen.com/api/documentation

Usage:

The functions in this library are:

Login
Token Refresh
Future Movement Algorithm
Future Movement Group Algorithm
Future Movement FX Algorithm
Future Movement Token Algorithm
Correlation Algorithm

The accepted licence types are:

Trial - t
Single User - s
Enterprise - e

Token Refresh


import thirtysevena as tsa

email = "name@email.com"
password = "notpassword"
licence = "e"

client = tsa.Client(email, password, licence)
client.token_refresh()

Future Movement Algorithm Example

import thirtysevena as tsa

email = "name@email.com"
password = "notpassword"
licence = "e"

client = tsa.Client(email, password, licence)
client.login()

data = {

"Metadata" : { "Label" : "Gamestop", "ID" : "Equity", "Timeframe" : "Daily" },

"P1" : { "Date" : "2020-12-01 00:00:00", "Open" : 17.11, "High" : 17.4, "Low" : 15.76, "Close" : 15.8 },

"P2" : { "Date" : "2020-12-02 00:00:00", "Open" : 15.7, "High" : 16.68, "Low" : 15.38, "Close" : 16.58 },

"P3" : { "Date" : "2020-12-03 00:00:00", "Open" : 16.48, "High" : 16.64, "Low" : 15.87, "Close" : 16.12 },

"P4" : { "Date" : "2020-12-04 00:00:00", "Open" : 16.3, "High" : 17.29, "Low" : 16.26, "Close" : 16.9 },

"P5" : { "Date" : "2020-12-07 00:00:00", "Open" : 17.0, "High" : 17.5, "Low" : 16.22, "Close" : 16.35 },

"P6" : { "Date" : "2020-12-08 00:00:00", "Open" : 16.37, "High" : 17.21, "Low" : 15.93, "Close" : 16.94 },

"P7" : { "Date" : "2020-12-09 00:00:00", "Open" : 13.92, "High" : 14.73, "Low" : 13.225, "Close" : 13.66 },

"P8" : { "Date" : "2020-12-10 00:00:00", "Open" : 13.12, "High" : 14.41, "Low" : 13.05, "Close" : 14.12 },

"P9" : { "Date" : "2020-12-11 00:00:00", "Open" : 13.91, "High" : 14.0, "Low" : 13.02, "Close" : 13.31 },

"P10" : { "Date" : "2020-12-14 00:00:00", "Open" : 13.34, "High" : 13.4335, "Low" : 12.14, "Close" : 12.72 },

"P11" : { "Date" : "2020-12-15 00:00:00", "Open" : 13.0 }

}

response = client.future_movement(data)

Future Movement Group Algorithm Example

import thirtysevena as tsa

email = "name@email.com"
password = "notpassword"
licence = "e"

client = tsa.Client(email, password, licence)
client.login()

data = {

"Metadata" : {"Label" : "Hypothetical Asset", "ID" : "Index", "Timeframe" : "3 Days" },

"P1" : { "Date" : "2020-02-03 00:00:00", "Open" : 26189.61, "High" : 26512.58, "Low" : 26145.59, "Close" : 26356.98 },

"P2" : { "Date" : "2020-02-04 00:00:00", "Open" : 26491.66, "High" : 26730.26, "Low" : 26491.66, "Close" : 26675.98 },

"P3" : { "Date" : "2020-02-05 00:00:00", "Open" : 26869.32, "High" : 26926.12, "Low" : 26641.92, "Close" : 26786.74 },

"P4" : { "Date" : "2020-02-06 00:00:00", "Open" : 27174.53, "High" : 27608.22, "Low" : 27029.49, "Close" : 27493.7 },

"P5" : { "Date" : "2020-02-07 00:00:00", "Open" : 27356.28, "High" : 27410.58, "Low" : 27224.12, "Close" : 27404.27 },

"P6" : { "Date" : "2020-02-10 00:00:00", "Open" : 27092.15, "High" : 27314.64, "Low" : 27044.88, "Close" : 27241.34 },

"P7" : { "Date" : "2020-02-11 00:00:00", "Open" : 27514.25, "High" : 27674.81, "Low" : 27436.99, "Close" : 27583.88 },

"P8" : { "Date" : "2020-02-12 00:00:00", "Open" : 27717.22, "High" : 27892.48, "Low" : 27614.83, "Close" : 27823.66 },

"P9" : { "Date" : "2020-02-13 00:00:00", "Open" : 27953.65, "High" : 27953.65, "Low" : 27695.6, "Close" : 27730.0 },

"P10" : { "Date" : "2020-02-14 00:00:00", "Open" : 27698.56, "High" : 27960.66, "Low" : 27695.59, "Close" : 27815.6 },

"P11" : { "Date" : "2020-02-17 00:00:00", "Open" : 27766.71, "High" : 28055.58, "Low" : 27766.71, "Close" : 27959.6 },

"P12" : { "Date" : "2020-02-18 00:00:00", "Open" : 27766.5, "High" : 27771.3, "Low" : 27496.25, "Close" : 27530.2 },

"P13" : { "Date" : "2020-02-19 00:00:00", "Open" : 27486.46, "High" : 27697.46, "Low" : 27448.6, "Close" : 27655.81 },

"P14" : { "Date" : "2020-02-20 00:00:00", "Open" : 27767.28, "High" : 27767.28, "Low" : 27383.13, "Close" : 27609.16 },

"P15" : { "Date" : "2020-02-21 00:00:00", "Open" : 27450.46, "High" : 27484.53, "Low" : 27264.78, "Close" : 27308.81 },

"P16" : { "Date" : "2020-02-24 00:00:00", "Open" : 27105.35, "High" : 27105.35, "Low" : 26813.22, "Close" : 26820.88 },

"P17" : { "Date" : "2020-02-25 00:00:00", "Open" : 26722.39, "High" : 26914.05, "Low" : 26667.04, "Close" : 26893.23 },

"P18" : { "Date" : "2020-02-26 00:00:00", "Open" : 26479.9, "High" : 26776.06, "Low" : 26479.9, "Close" : 26696.49 },

"P19" : { "Date" : "2020-02-27 00:00:00", "Open" : 26529.17, "High" : 26849.57, "Low" : 26419.97, "Close" : 26778.62 },

"P20" : { "Date" : "2020-02-28 00:00:00", "Open" : 26249.06, "High" : 26313.55, "Low" : 25989.41, "Close" : 26129.93 },

"P21" : { "Date" : "2020-03-02 00:00:00", "Open" : 26077.73, "High" : 26375.91, "Low" : 26077.73, "Close" : 26291.68 },

"P22" : { "Date" : "2020-03-03 00:00:00", "Open" : 26419.13, "High" : 26527.75, "Low" : 26233.39, "Close" : 26284.82 },

"P23" : { "Date" : "2020-03-04 00:00:00", "Open" : 26321.56, "High" : 26372.48, "Low" : 26038.39, "Close" : 26222.07 },

"P24" : { "Date" : "2020-03-05 00:00:00", "Open" : 26348.16, "High" : 26805.58, "Low" : 26315.36, "Close" : 26767.87 },

"P25" : { "Date" : "2020-03-06 00:00:00", "Open" : 26397.78, "High" : 26408.8, "Low" : 26084.23, "Close" : 26146.67 },

"P26" : { "Date" : "2020-03-09 00:00:00", "Open" : 25134.02, "High" : 25321.28, "Low" : 24948.38, "Close" : 25040.46 },

"P27" : { "Date" : "2020-03-10 00:00:00", "Open" : 25285.68, "High" : 25578.61, "Low" : 24978.97, "Close" : 25392.51 },

"P28" : { "Date" : "2020-03-11 00:00:00", "Open" : 25459.96, "High" : 25493.23, "Low" : 25140.38, "Close" : 25231.61 },

"P29" : { "Date" : "2020-03-12 00:00:00", "Open" : 24657.67, "High" : 24657.67, "Low" : 24117.94, "Close" : 24309.07 },

"P30" : { "Date" : "2020-03-13 00:00:00", "Open" : 22519.32, "High" : 24184.48, "Low" : 22519.32, "Close" : 24032.91 },

"P31" : { "Date" : "2020-03-16 00:00:00", "Open" : 23317.81 }

}

response = client.future_movement_group(data)

Future Movement FX Algorithm Example

import thirtysevena as tsa

email = "name@email.com"
password = "notpassword"
licence = "e"

client = tsa.Client(email, password, licence)
client.login()

data = {

"Metadata" : {"Label" : "GBP/EUR", "ID" : "FX", "Timeframe" : "Daily" },

"P1" : { "Date" : "2021-02-08 00:00:00", "Open" : 1.1401, "High" : 1.1412, "Low" : 1.1373, "Close" : 1.1401 },

"P2" : { "Date" : "2021-02-09 00:00:00", "Open" : 1.1403, "High" : 1.1411, "Low" : 1.1366, "Close" : 1.1404 },

"P3" : { "Date" : "2021-02-10 00:00:00", "Open" : 1.1402, "High" : 1.1425, "Low" : 1.1387, "Close" : 1.14 },

"P4" : { "Date" : "2021-02-11 00:00:00", "Open" : 1.1411, "High" : 1.1424, "Low" : 1.1381, "Close" : 1.1411 },

"P5" : { "Date" : "2021-02-12 00:00:00", "Open" : 1.1385, "High" : 1.1434, "Low" : 1.1372, "Close" : 1.1383 },

"P6" : { "Date" : "2021-02-15 00:00:00", "Open" : 1.1442, "High" : 1.147, "Low" : 1.1439, "Close" : 1.1441 },

"P7" : { "Date" : "2021-02-16 00:00:00", "Open" : 1.1467, "High" : 1.1495, "Low" : 1.1441, "Close" : 1.1464 },

"P8" : { "Date" : "2021-02-17 00:00:00", "Open" : 1.1486, "High" : 1.1519, "Low" : 1.1467, "Close" : 1.1484 },

"P9" : { "Date" : "2021-02-18 00:00:00", "Open" : 1.151, "High" : 1.1569, "Low" : 1.1491, "Close" : 1.151 },

"P10" : { "Date" : "2021-02-19 00:00:00", "Open" : 1.1551, "High" : 1.1568, "Low" : 1.1523, "Close" : 1.1551 },

"P11" : { "Date" : "2021-02-22 00:00:00", "Open" : 1.1569 }

}

response = client.future_movement_fx(data)

Future Movement Token Algorithm Example

import thirtysevena as tsa

email = "name@email.com"
password = "notpassword"
licence = "e"

client = tsa.Client(email, password, licence)
client.login()

data = {

"Metadata" : {"Label" : "Example Token", "ID" : "Crypto Token", "Timeframe" : "Daily" },

"P1" : { "Date" : "2021-07-26 00:00:00", "Open " : 1.174122334, "High" : 1.174398184, "Low" : 1.174122334, "Close" : 1.174260259 },

"P2" : { "Date" : "2021-07-27 00:00:00", "Open " : 1.174260259, "High" : 1.174260259, "Low" : 1.174122334, "Close" : 1.174122334 },

"P3" : { "Date" : "2021-07-28 00:00:00", "Open " : 1.174122334, "High" : 1.174398184, "Low" : 1.174122334, "Close" : 1.174122334 },

"P4" : { "Date" : "2021-07-29 00:00:00", "Open " : 1.174398184, "High" : 1.174398184, "Low" : 1.173984528, "Close" : 1.173984528 },

"P5" : { "Date" : "2021-07-30 00:00:00", "Open " : 1.173984528, "High" : 1.174398184, "Low" : 1.173984528, "Close" : 1.173984528 },

"P6" : { "Date" : "2021-08-02 00:00:00", "Open " : 1.173984528, "High" : 1.174260259, "Low" : 1.173984528, "Close" : 1.173984528 },

"P7" : { "Date" : "2021-08-03 00:00:00", "Open " : 1.173846722, "High" : 1.173984528, "Low" : 1.173846722, "Close" : 1.173984528 },

"P8" : { "Date" : "2021-08-04 00:00:00", "Open " : 1.173846722, "High" : 1.174260259, "Low" : 1.173846722, "Close" : 1.173846722 },

"P9" : { "Date" : "2021-08-05 00:00:00", "Open " : 1.174260259, "High" : 1.174260259, "Low" : 1.173984528, "Close" : 1.173984528 },

"P10" : { "Date" : "2021-08-06 00:00:00", "Open " : 1.173984528, "High" : 1.173984528, "Low" : 1.173984528, "Close" : 1.173984528 },

"P11" : { "Date" : "2021-08-07 00:00:00", "Open " : 1.173846722 }

}

response = client.future_movement_token(data)

Future Movement Correlation Algorithm Example

import thirtysevena as tsa

email = "name@email.com"
password = "notpassword"
licence = "e"

client = tsa.Client(email, password, licence)
client.login()

data = {

"Metadata" : { "A_Label" : "Asset 1", "B_Label" : "Asset 2", "Timeframe" : "Daily"},

"A_P1" : { "Date" : "2000-01-01 00:00:00", "Open" : 364.02, "High" : 364.31, "Low" : 364.01, "Close" : 364.31 },

"A_P2" : { "Date" : "2000-01-02 00:00:00", "Open" : 364.31, "High" : 364.58, "Low" : 364.01, "Close" : 364.01 },

"A_P3" : { "Date" : "2000-01-03 00:00:00", "Open" : 364.01, "High" : 364.21, "Low" : 364.01, "Close" : 364.11 },

"B_P1" : { "Date" : "2000-01-01 00:00:00", "Open" : 364.02, "High" : 364.31, "Low" : 364.01, "Close" : 364.31 },

"B_P2" : { "Date" : "2000-01-02 00:00:00", "Open" : 364.31, "High" : 364.58, "Low" : 364.01, "Close" : 364.01 },

"B_P3" : { "Date" : "2000-01-03 00:00:00", "Open" : 364.01, "High" : 364.21, "Low" : 364.01, "Close" : 364.11 }

}

response = client.correlation(data)

License

This repository is licensed under MIT license. See LICENSE for details.

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

This version

1.0

Sep 26, 2021

0.0.0

Aug 22, 2021

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

thirtysevena-1.0.tar.gz (6.8 kB view details)

Uploaded Sep 26, 2021 Source

File details

Details for the file thirtysevena-1.0.tar.gz.

File metadata

Download URL: thirtysevena-1.0.tar.gz
Upload date: Sep 26, 2021
Size: 6.8 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.23.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.8.5

File hashes

Hashes for thirtysevena-1.0.tar.gz
Algorithm	Hash digest
SHA256	`cab51f1f632df9ae56bd2d0d7ab0d4dc204c214494b50412cd70dd26f99287eb`
MD5	`0c5d052b8ee0cd79cef23e677099ec0f`
BLAKE2b-256	`56f19af0d6b8ff7d9baca9a5b8241841f54cdc56f4615ced76cc9aafa801a9fc`