| Name | priceindices JSON |
| Version |
1.4.0
JSON |
| download |
| home_page | |
| Summary | A python package to get historical market data of cryptocurrencies, and calculate & plot different price technical indicators. |
| upload_time | 2023-02-01 14:44:45 |
| maintainer | |
| docs_url | None |
| author | Dayal Chand Aichara |
| requires_python | >=3.8,<4.0 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/dc-aichara/PriceIndices)
## Development Environment
### Poetry
* Install Poetry
```shell
curl -sSL https://install.python-poetry.org | python3 -
```
* Install dependencies
```shell
poetry install
```
* To add new dependencies use `poetry add`
```shell
poetry add dependency_name
```
* Read `Poetry` [documentation](https://python-poetry.org/docs/) for more.
## Installation
### pip
```
pip install PriceIndics
```
### Poetry
```
poetry add PriceIndices
```
### From Source (Github)
git clone https://github.com/dc-aichara/Price-Indices.git
cd Price-Indices
python3 setup.py install
## Usages
```python
from PriceIndices import MarketHistory, Indices
```
## Examples
- #### Get market history and closing price
```python
>>> history = MarketHistory()
# Get Market History
>>> df_history = history.get_history("bitcoin", "2020-03-16", "2021-03-15")
>>> df_history.head()
open high low close volume market_cap date
0 59267.429049 60540.992712 55393.165363 55907.200226 6.641937e+10 1.042946e+12 2021-03-15
1 61221.134297 61597.918396 59302.316977 59302.316977 4.390123e+10 1.106226e+12 2021-03-14
2 57343.370247 61683.864014 56217.972382 61243.084766 6.066983e+10 1.142369e+12 2021-03-13
3 57821.218747 57996.619490 55376.650088 57332.088964 5.568994e+10 1.069366e+12 2021-03-12
4 55963.180089 58091.062703 54484.593089 57805.123019 5.677234e+10 1.078136e+12 2021-03-11
# Get closing price
>>> price_data = history.get_price("bitcoin", "2020-03-16", "2021-03-15")
>>> price_data.head()
date price
0 2021-03-15 55907.200226
1 2021-03-14 59302.316977
2 2021-03-13 61243.084766
3 2021-03-12 57332.088964
4 2021-03-11 57805.123019
```
- #### Calculate Volatility Index
```python
indices = Indices(df=price_data, plot_dir="plots")
>>> df_bvol = indices.get_vola_index(
plot=True,
plot_name="vola_index.png",
show_plot=False
)
>>> df_bvol.head()
date price BVOL_Index
0 2019-10-29 9427.69 0.711107
1 2019-10-28 9256.15 0.707269
2 2019-10-27 9551.71 0.709765
3 2019-10-26 9244.97 0.698544
4 2019-10-25 8660.70 0.692656
```
- #### Plot Volatility Index
Plot will be saved in plots directory as `vola_index.png`.
<img src= 'plots/bvol_index.png' >
- #### Calculate Relative Strength Index (RSI)
```python
>>> df_rsi = indices.get_rsi(
plot=True,
plot_name="rsi.png",
show_plot=False,
)
>>> print(df_rsi.head())
date price RSI_1 RS_Smooth RSI_2
0 2019-10-30 9205.73 64.641855 1.624958 61.904151
1 2019-10-29 9427.69 65.707097 1.709072 63.086984
2 2019-10-28 9256.15 61.333433 1.597755 61.505224
3 2019-10-27 9551.71 66.873327 2.012345 66.803267
4 2019-10-26 9244.97 63.535368 1.791208 64.173219
```
- #### Plot RSI
Plot will be saved in plots directory as `rsi.png`.
<img src='plots/rsi.png' >
- #### Get Bollinger Bands and its plot
```python
>>> df_bb = indices.get_bollinger_bands(
days=20,
plot=True,
plot_name="bollinger_bands.png",
show_plot=False,
)
>>> df_bb.head()
date price BB_upper BB_lower
0 2019-10-30 9205.73 9635.043581 -8428.5855
1 2019-10-29 9427.69 9550.707153 -8397.6225
2 2019-10-28 9256.15 9408.263164 -8356.0250
3 2019-10-27 9551.71 9268.466516 -8304.6565
4 2019-10-26 9244.97 9003.752779 -8239.3520
"""
This will also save Bollingers bands plot in your working directory as 'bollinger_bands.png' in plots folder.
"""
```
<img src='plots/bollinger_bands.png'>
- #### Get Moving Average Convergence Divergence (MACD) and its plot
```python
>>> df_macd = indices.get_moving_average_convergence_divergence(
plot=True,
plot_name="macd.png",
show_plot=False,
)
"""
This will return a pandas DataFrame and save EMA plot as 'macd.png' in in plots folder.
""""
>>> df_macd.head()
date price MACD
0 2019-10-30 9205.73 0.000000
1 2019-10-29 9427.69 17.706211
2 2019-10-28 9256.15 17.692715
3 2019-10-27 9551.71 41.057952
4 2019-10-26 9244.97 34.426864
```
<img src='plots/macd.png'>
- #### Get Simple Moving Average (SMA) and its plot
```python
>>> df_sma = indices.get_simple_moving_average(
days=20,
plot=True,
plot_name="sma.png",
show_plot=False,
)
"""This will return a pandas DataFrame and save EMA plot as 'sma.png' in plots folder.
""""
>>> df_sma.head()
date price SMA
0 2019-10-30 9205.73 8467.488000
1 2019-10-29 9427.69 8400.797333
2 2019-10-28 9256.15 8330.597333
3 2019-10-27 9551.71 8268.254667
4 2019-10-26 9244.97 8187.244667
```
<img src='plots/sma.png'>
- ### Get Exponential Moving Average (EMA) and its plot
```python
>>> df_ema = indices.get_exponential_moving_average(
periods=(20,70),
plot=True,
plot_name="ema.png",
show_plot=False,
)
"""This will return a pandas DataFrame and save EMA plot as 'ema.png' in plots folder.
""""
>>> df_ema.head()
date price EMA_20 EMA_70
0 2019-10-30 9205.73 9205.730000 9205.730000
1 2019-10-29 9427.69 9226.869048 9211.982394
2 2019-10-28 9256.15 9229.657710 9213.226552
3 2019-10-27 9551.71 9260.329356 9222.761297
4 2019-10-26 9244.97 9258.866561 9223.386895
>>>
```
<img src='plots/ema.png' >
### License
[MIT](https://choosealicense.com/licenses/mit/) © [Dayal Chand Aichara](https://github.com/dc-aichara)
### Check out [webpage](https://dc-aichara.github.io/PriceIndices/) of PriceIndices package.
### I have created a [cryptocurrency technical indicators dashboard](https://crypto-indicators-dashboard.herokuapp.com/) which uses this library.
### Disclaimer:
```
All content provided here, is for educational purpose and your general information only, procured from third party sources.
I make no warranties of any kind in relation to this content, including but not limited to accuracy
and updatedness. No part of the content that I provide constitutes financial advice, legal advice
or any other form of advice meant for your specific reliance for any purpose. Any use or reliance on
my content is solely at your own risk and discretion. You should conduct your own research, review,
analyse and verify my content before relying on them. Trading is a highly risky activity that can
lead to major losses, please therefore consult your financial advisor before making any decision.
No content on this Site is meant to be a solicitation or offer.
```
Raw data
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"description": "\n\n[](https://github.com/dc-aichara/PriceIndices)\n\n\n## Development Environment\n\n### Poetry\n * Install Poetry\n ```shell\n curl -sSL https://install.python-poetry.org | python3 -\n\n ```\n * Install dependencies\n ```shell\n poetry install\n ```\n * To add new dependencies use `poetry add` \n ```shell\n poetry add dependency_name\n ```\n * Read `Poetry` [documentation](https://python-poetry.org/docs/) for more.\n\n## Installation \n\n### pip \n\n```\npip install PriceIndics\n```\n\n### Poetry\n\n```\npoetry add PriceIndices\n\n```\n\n### From Source (Github)\n \n git clone https://github.com/dc-aichara/Price-Indices.git\n \n cd Price-Indices \n \n python3 setup.py install\n \n## Usages \n \n```python\nfrom PriceIndices import MarketHistory, Indices\n\n```\n## Examples \n\n- #### Get market history and closing price\n\n```python\n>>> history = MarketHistory()\n\n# Get Market History \n\n>>> df_history = history.get_history(\"bitcoin\", \"2020-03-16\", \"2021-03-15\") \n>>> df_history.head()\n open high low close volume market_cap date\n0 59267.429049 60540.992712 55393.165363 55907.200226 6.641937e+10 1.042946e+12 2021-03-15\n1 61221.134297 61597.918396 59302.316977 59302.316977 4.390123e+10 1.106226e+12 2021-03-14\n2 57343.370247 61683.864014 56217.972382 61243.084766 6.066983e+10 1.142369e+12 2021-03-13\n3 57821.218747 57996.619490 55376.650088 57332.088964 5.568994e+10 1.069366e+12 2021-03-12\n4 55963.180089 58091.062703 54484.593089 57805.123019 5.677234e+10 1.078136e+12 2021-03-11\n\n# Get closing price\n\n>>> price_data = history.get_price(\"bitcoin\", \"2020-03-16\", \"2021-03-15\") \n\n>>> price_data.head()\n date price\n0 2021-03-15 55907.200226\n1 2021-03-14 59302.316977\n2 2021-03-13 61243.084766\n3 2021-03-12 57332.088964\n4 2021-03-11 57805.123019\n\n```\n\n- #### Calculate Volatility Index\n\n```python\nindices = Indices(df=price_data, plot_dir=\"plots\")\n>>> df_bvol = indices.get_vola_index(\n plot=True,\n plot_name=\"vola_index.png\",\n show_plot=False \n) \n>>> df_bvol.head()\n date price BVOL_Index\n0 2019-10-29 9427.69 0.711107\n1 2019-10-28 9256.15 0.707269\n2 2019-10-27 9551.71 0.709765\n3 2019-10-26 9244.97 0.698544\n4 2019-10-25 8660.70 0.692656\n\n```\n\n- #### Plot Volatility Index\n Plot will be saved in plots directory as `vola_index.png`.\n\n\n<img src= 'plots/bvol_index.png' >\n\n- #### Calculate Relative Strength Index (RSI)\n\n```python\n\n>>> df_rsi = indices.get_rsi(\n plot=True,\n plot_name=\"rsi.png\",\n show_plot=False,\n) \n\n>>> print(df_rsi.head())\n date price RSI_1 RS_Smooth RSI_2\n0 2019-10-30 9205.73 64.641855 1.624958 61.904151\n1 2019-10-29 9427.69 65.707097 1.709072 63.086984\n2 2019-10-28 9256.15 61.333433 1.597755 61.505224\n3 2019-10-27 9551.71 66.873327 2.012345 66.803267\n4 2019-10-26 9244.97 63.535368 1.791208 64.173219\n\n\n```\n\n- #### Plot RSI\nPlot will be saved in plots directory as `rsi.png`.\n\n\n<img src='plots/rsi.png' >\n\n- #### Get Bollinger Bands and its plot\n\n```python\n>>> df_bb = indices.get_bollinger_bands(\n days=20, \n plot=True,\n plot_name=\"bollinger_bands.png\",\n show_plot=False,\n ) \n>>> df_bb.head()\n date price BB_upper BB_lower\n0 2019-10-30 9205.73 9635.043581 -8428.5855\n1 2019-10-29 9427.69 9550.707153 -8397.6225\n2 2019-10-28 9256.15 9408.263164 -8356.0250\n3 2019-10-27 9551.71 9268.466516 -8304.6565\n4 2019-10-26 9244.97 9003.752779 -8239.3520\n\n\n\"\"\"\nThis will also save Bollingers bands plot in your working directory as 'bollinger_bands.png' in plots folder.\n\"\"\"\n\n```\n\n<img src='plots/bollinger_bands.png'>\n\n\n- #### Get Moving Average Convergence Divergence (MACD) and its plot\n\n```python\n\n>>> df_macd = indices.get_moving_average_convergence_divergence(\n plot=True,\n plot_name=\"macd.png\",\n show_plot=False,\n)\n\"\"\"\nThis will return a pandas DataFrame and save EMA plot as 'macd.png' in in plots folder. \n\"\"\"\"\n>>> df_macd.head()\n date price MACD\n0 2019-10-30 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plots folder.\n\"\"\"\"\n\n>>> df_ema.head()\n date price EMA_20 EMA_70\n0 2019-10-30 9205.73 9205.730000 9205.730000\n1 2019-10-29 9427.69 9226.869048 9211.982394\n2 2019-10-28 9256.15 9229.657710 9213.226552\n3 2019-10-27 9551.71 9260.329356 9222.761297\n4 2019-10-26 9244.97 9258.866561 9223.386895\n>>> \n\n\n```\n\n<img src='plots/ema.png' >\n\n### License\n \n[MIT](https://choosealicense.com/licenses/mit/) \u00a9 [Dayal Chand Aichara](https://github.com/dc-aichara)\n\n\n### Check out [webpage](https://dc-aichara.github.io/PriceIndices/) of PriceIndices package. \n\n### I have created a [cryptocurrency technical indicators dashboard](https://crypto-indicators-dashboard.herokuapp.com/) which uses this library. \n### Disclaimer: \n\n```\nAll content provided here, is for educational purpose and your general information only, procured from third party sources.\nI make no warranties of any kind in relation to this content, including but not limited to accuracy\nand updatedness. No part of the content that I provide constitutes financial advice, legal advice \nor any other form of advice meant for your specific reliance for any purpose. Any use or reliance on\nmy content is solely at your own risk and discretion. You should conduct your own research, review, \nanalyse and verify my content before relying on them. Trading is a highly risky activity that can \nlead to major losses, please therefore consult your financial advisor before making any decision.\nNo content on this Site is meant to be a solicitation or offer.\n```\n\n",
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