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🔮 feat: predict signal 🔮 (#124)
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* almost whole model plan

* save data

* basically done

* actually done w model creation

* ready to predict

* more done

* new file

* modify model

* prediction trial

* again

* use metadata

* make sure data is non-null

* trade

* typo

* fix df

* don't trigger every push

* done

* fix fb
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suchak1 authored Nov 8, 2021
1 parent 138a0eb commit 762e8d8
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9 changes: 0 additions & 9 deletions .github/workflows/model.yml
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Expand Up @@ -12,14 +12,6 @@ on:
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
include:
- pca: 2
- pca: 3
- pca: 5
- pca: 7
fail-fast: false
steps:
- name: Checkout repo
uses: actions/checkout@v2
Expand Down Expand Up @@ -52,7 +44,6 @@ jobs:
- name: Create model
env:
PCA: ${{ matrix.pca }}
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }}
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59 changes: 59 additions & 0 deletions .github/workflows/predict.yml
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@@ -0,0 +1,59 @@
# This workflow will install Python dependencies, run tests and lint with a single version of Python
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions

name: Predict Signal

on:
# push:
# branches:
# - feature/predict
schedule:
- cron: "0 2 * * *"
# 9pm EST every day
workflow_dispatch:

jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout repo
uses: actions/checkout@v2

- name: Set up Python 3.8
uses: actions/setup-python@v2
with:
python-version: 3.8

- name: Cache pip dependencies
uses: actions/cache@v2
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install flake8 pytest coverage
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Decrypt script
env:
RH_PASSWORD: ${{ secrets.RH_PASSWORD }}
SALT: ${{ secrets.SALT }}
run: |
python util/decrypt.py encrypted/predict_signal.py
- name: Predict signal
env:
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }}
S3_BUCKET: ${{ secrets.S3_BUCKET }}
POLYGON: ${{ secrets.POLYGON }}
GLASSNODE: ${{ secrets.GLASSNODE }}
BINANCE_KEY: ${{ secrets.BINANCE_KEY }}
BINANCE_SECRET: ${{ secrets.BINANCE_SECRET }}
run: |
python encrypted/predict_signal.py
10 changes: 9 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -111,4 +111,12 @@ Using Robinhood 2FA, we can simply provide our MFA one-time password in the `.en
<!-- auto sklearn -->
<!-- get data, make decision/trade, update model before 11pm est each night -->
<!-- 8:30pm est polygon, 9pm est glassnode -->
<!-- auto-encrypt and run on save extensions for encrypting model -->

<!-- 2. connect model output to buy/sell ordering - update csvs with model result and order summary (signals.csv, orders.csv) -->
<!-- 3. get new data and model input every day at 9pm? and order -->

<!-- extra -->
<!-- abstract away undersample fx from preprocess fx, and buy and sell from order fx, make oracle class -->
<!-- 4. automate saving model and preprocessors (every 2 weeks ) -->
<!-- 5. add live results on website / model vs buying and holding like alphahub - use dash or plotly? use pca visualization, tsne for higher dimensions, roc curve, etc-->
<!-- 6. add authentication and business like report style like in dash example -->
2 changes: 1 addition & 1 deletion encrypted/create_model.py.encrypted
Original file line number Diff line number Diff line change
@@ -1 +1 @@
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1 change: 1 addition & 0 deletions requirements.txt
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ polygon-api-client == 0.2.11
pytz == 2021.3
vectorbt == 0.21.0
scipy == 1.7.1
scikit-learn == 0.24.2
auto-sklearn == 0.14.0
cryptography == 35.0.0
ta == 0.7.0
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3 changes: 3 additions & 0 deletions src/Constants.py
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Expand Up @@ -84,6 +84,9 @@ def get_env_bool(var_name):
# SOPR
SOPR = 'SOPR'

# Signals
SIG = 'Sig'

# Misc
POLY_CRYPTO_SYMBOLS = [
'X%3ABTCUSD', 'X%3AETHUSD',
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