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MDUYNMDUYN
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Fix tests and workflows
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‎.github/workflows/publish.yml‎

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name: Build and publish python package
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on:
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release:
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types: [ published ]
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jobs:
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publish-service-client-package:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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steps:
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- name: Publish PyPi package
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uses: code-specialist/pypi-poetry-publish@v1
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with:
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ACCESS_TOKEN: ${{ secrets.REPOSITORY_ACCESS_TOKEN }}
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PUBLISH_REGISTRY_PASSWORD: ${{ secrets.PYPI_TOKEN }}
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BRANCH: "main"
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POETRY_VERSION: "1.7.1"
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POETRY_CORE_VERSION: "1.8.1"

‎.github/workflows/test.yml‎

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name: test
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on:
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push:
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branches:
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- '*' # matches every branch that doesn't contain a '/'
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- '*/*' # matches every branch containing a single '/'
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- '**' # matches every branch
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pull_request:
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branches:
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- '*' # matches every branch that doesn't contain a '/'
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- '*/*' # matches every branch containing a single '/'
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- '**' # matches every branch
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jobs:
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linting:
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runs-on: ubuntu-latest
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steps:
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#----------------------------------------------
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# check-out repo and set-up python
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#----------------------------------------------
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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#----------------------------------------------
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# load pip cache if cache exists
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#----------------------------------------------
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- uses: actions/cache@v3
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with:
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path: ~/.cache/pip
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key: ${{ runner.os }}-pip
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restore-keys: ${{ runner.os }}-pip
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#----------------------------------------------
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# install and run linters
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#----------------------------------------------
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- run: python -m pip install black flake8 isort
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- run: |
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flake8 ./investing_algorithm_framework
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test:
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needs: linting
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strategy:
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fail-fast: true
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matrix:
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os: [ "ubuntu-latest", "macos-latest", "windows-latest" ]
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python-version: [ "3.8", "3.9", "3.10", "3.11" ]
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defaults:
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run:
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shell: bash
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runs-on: ${{ matrix.os }}
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steps:
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#----------------------------------------------
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# check-out repo and set-up python
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#----------------------------------------------
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Set up python ${{ matrix.python-version }}
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id: setup-python
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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#----------------------------------------------
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# ----- install distutils if needed -----
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#----------------------------------------------
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- name: Install distutils on Ubuntu
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if: matrix.os == 'ubuntu-latest'
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run: |
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sudo add-apt-repository ppa:deadsnakes/ppa
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sudo apt-get update
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sudo apt install python${{ matrix.python-version }}-distutils
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#----------------------------------------------
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# ----- install & configure poetry -----
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#----------------------------------------------
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- name: Install Poetry
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uses: snok/install-poetry@v1
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with:
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version: 1.7.1
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virtualenvs-create: true
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virtualenvs-in-project: true
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#----------------------------------------------
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# load cached venv if cache exists
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#----------------------------------------------
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- name: Load cached venv
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id: cached-poetry-dependencies
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uses: actions/cache@v3
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with:
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path: .venv
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key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
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#----------------------------------------------
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# install dependencies if cache does not exist
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#----------------------------------------------
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- name: Install dependencies
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if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
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run: |
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poetry install --no-interaction --no-root
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#----------------------------------------------
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# install your root project, if required
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#----------------------------------------------
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- name: Install library
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run: |
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poetry install --no-interaction
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#----------------------------------------------
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# add matrix specifics and run test suite
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#----------------------------------------------
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- name: Run tests
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run: |
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source $VENV
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coverage run -m unittest discover -s tests
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# #----------------------------------------------
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# # upload coverage stats
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# #----------------------------------------------
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# - name: Upload coverage
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# uses: codecov/codecov-action@v3
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# with:
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# file: ./coverage.xml
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# fail_ci_if_error: true

‎pyindicators/indicators/exponential_moving_average.py‎

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from typing import Union
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from pandas import DataFrame as PdDataFrame
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from polars import DataFrame as PlDataFrame
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import polars as pl
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from pyindicators.exceptions import PyIndicatorException
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def ema(

‎tests/indicators/test_ema.py‎

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from datetime import timedelta
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from unittest import TestCase
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import pandas as pd
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import talib as ta
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import numpy as np
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import tulipy as ti
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from investing_algorithm_framework import CSVOHLCVMarketDataSource
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import polars as pl
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import pandas.testing as pdt
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from polars.testing import assert_frame_equal
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from tests.resources import TestBaseline
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from pyindicators import ema
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class Test(TestBaseline):
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correct_output_csv_filename = \
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"EMA_200_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
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def generate_pandas_df(self, polars_source_df):
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polars_source_df = ema(
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data=polars_source_df,
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period=200,
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result_column="EMA_200",
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source_column="Close"
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)
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return polars_source_df
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def generate_polars_df(self, pandas_source_df):
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pandas_source_df = ema(
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data=pandas_source_df,
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period=200,
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result_column="EMA_200",
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source_column="Close"
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)
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return pandas_source_df
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def test_comparison_pandas(self):
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import pyindicators as pyi
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# Load the correct output in a pandas dataframe
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correct_output_pd = pd.read_csv(self.get_correct_output_csv_path())
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# Load the source in a pandas dataframe
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source = pd.read_csv(self.get_source_csv_path())
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class Test(TestCase):
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# Generate the pandas dataframe
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output = self.generate_pandas_df(source)
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output = output[correct_output_pd.columns]
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output["Datetime"] = \
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pd.to_datetime(output["Datetime"]).dt.tz_localize(None)
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correct_output_pd["Datetime"] = \
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pd.to_datetime(correct_output_pd["Datetime"]).dt.tz_localize(None)
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def test(self):
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data_source = CSVOHLCVMarketDataSource(
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csv_file_path="../test_data/OHLCV_BTC-EUR_BINANCE_15m"
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"_2023-12-01:00:00_2023-12-25:00:00.csv",
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pdt.assert_frame_equal(correct_output_pd, output)
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def test_comparison_polars(self):
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# Load the correct output in a polars dataframe
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correct_output_pl = pl.read_csv(self.get_correct_output_csv_path())
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# Load the source in a polars dataframe
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source = pl.read_csv(self.get_source_csv_path())
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# Generate the polars dataframe
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output = self.generate_polars_df(source)
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# Convert the datetime columns to datetime
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# Convert the 'Datetime' column in both DataFrames to datetime
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output = output.with_columns(
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pl.col("Datetime").str.strptime(pl.Datetime).alias("Datetime")
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)
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data_source.end_date = data_source.start_date \
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+ timedelta(days=4, hours=4)
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while not data_source.empty():
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data = data_source.get_data(market_credential_service=None)
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df = pd.DataFrame(
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data,
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columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
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)
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pyi_ema = pyi.ema(series=df["Close"], period=200)
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ta_ema = ta.EMA(df["Close"], timeperiod=200).astype('float64')
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ti_ema = pd.Series(ti.ema(df["Close"].to_numpy(), period=200))
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# Define a tolerance for comparison
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tolerance = 1e-9
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print(ta_ema.iloc[-1], ti_ema.iloc[-1])
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# Compare the two Series with tolerance
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# nan_mask = ~np.isnan(ta_sma) & ~np.isnan(pyi_sma)
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# comparison_result = np.abs(
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# ta_sma[nan_mask] - pyi_sma[nan_mask]) <= tolerance
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# data_source.start_date = \
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# data_source.start_date + timedelta(minutes=15)
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# data_source.end_date = data_source.end_date + timedelta(minutes=15)
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# self.assertTrue(all(comparison_result))
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correct_output_pl = correct_output_pl.with_columns(
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pl.col("Datetime").str.strptime(pl.Datetime).alias("Datetime")
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)
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output = output[correct_output_pl.columns]
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output = self.make_polars_column_datetime_naive(output, "Datetime")
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correct_output_pl = self.make_polars_column_datetime_naive(
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correct_output_pl, "Datetime"
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)
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assert_frame_equal(correct_output_pl, output)

‎tests/resources/__init__.py‎

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from .test_baseline import TestBaseline
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__all__ = [
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"TestBaseline",
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]

‎tests/resources/test_baseline.py‎

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import os
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import polars as pl
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from abc import abstractmethod
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from unittest import TestCase
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class TestBaseline(TestCase):
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correct_output_csv_filename = None
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source_csv_filename = \
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"OHLCV_BTC-EUR_BINANCE_15m_2023-12-01:00:00_2023-12-25:00:00.csv"
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result_column = None
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@abstractmethod
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def generate_polars_df(self, pandas_source_df):
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pass
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@abstractmethod
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def generate_pandas_df(self, polars_source_df):
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pass
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def make_polars_column_datetime_naive(self, df: pl.DataFrame, column: str) -> pl.DataFrame:
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# Ensure the column is in the correct type, then convert to UTC and remove timezone
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return df.with_columns(
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pl.col(column).cast(pl.Datetime).dt.convert_time_zone("UTC").cast(pl.Datetime).alias(column)
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)
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def get_correct_output_csv_path(self):
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return os.path.abspath(
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os.path.join(
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os.path.join(
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os.path.join(
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os.path.join(
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os.path.join(
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os.path.realpath(__file__),
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os.pardir
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),
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os.pardir
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),
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"test_data"
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),
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"correct_test_data"
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),
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self.correct_output_csv_filename
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)
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)
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def get_source_csv_path(self):
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return os.path.abspath(
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os.path.join(
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os.path.join(
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os.path.join(
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os.path.join(
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os.path.join(
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os.path.realpath(__file__),
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os.pardir
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),
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os.pardir
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),
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"test_data"
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),
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"source_test_data"
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),
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self.source_csv_filename
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)
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)

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