pythoncoin 2.0.3
a lovable data analysis and algorithmic trading library for cryptocurrencies,including tools for deploying and analyzing any strategy
License: MIT
Requires Python: >=3.10

![pycoin](https://github.com/hadif1999/pycoin/blob/master/pics/pycoin_logo_raw_nobg.png?raw=true)
# Pycoin

### a lovable data analysis and algorithmic trading library for cryptocurrencies :moneybag:
including tools for deploying any strategy including pattern based strategies,
Price Action strategies, Indicator based strategies and also Machine learning based strategies. 
able to run multi strategy instances on a single bot as a webapp and a lot more...
### what can this package do:
- [x] download market historical data for all symbols from almost all exchanges thanks to [ccxt](https://github.com/ccxt/ccxt) :chart_with_upwards_trend: 
- [x] visualizing capabilities to easily analyze market using [plotly](https://github.com/plotly/plotly.py) :chart_with_downwards_trend:
- [x] able to perform some useful analysis such as finding market trend according to market past high and lows, finding market important levels (like support and resistance) and more :bar_chart:
- [ ] able to define your strategy, backtest it, run it in dry run mode and also in real mode :mag: (soon)
- [ ] using telegram bot and webapp to control and monitor your bot :robot: (soon)  
- [ ] run multiple strategy instances for each user as a single bot. (soon) 

>[!NOTE]
>**for usage examples please checkout [examples](https://github.com/hadif1999/pycoin/tree/master/examples) folder and open provided notebooks.**

## Installation

#### via cloning (recommended)
```bash
pip install -e git+https://github.com/hadif1999/pycoin.git#egg="pythoncoin" 
```
if you need extra dependencies such as ploting or AI packages add **[extra]**(name of extra dependency that will be mentioned below) to end of "pythoncoin" (keep using quotes)

example of installing plotting and jupyter dependencies:
```bash
pip install -e git+https://github.com/hadif1999/pycoin.git#egg="pythoncoin[jupyter,plot]" 
```
**please be careful not to use spaces between extra packages list**

available extra packages:
- **plot**: installs packages related to plotting.
- **jupyter**: installs packages related to using in jupyter notebook.
- **ai**: installs packages related to using AI features.
- **hdf5**: installs packages related to big data features.
- **all**: installs all available dependencies.

#### installing from PyPI
##### standard dependencies
```bash
!pip install pythoncoin
```
##### installation with extra dependencies
```bash 
!pip install pythoncoin[plot] 
```
as mentioned earlier you can also use **ai**, **plot**, **jupyter**, **hdf5** or **all** to install needed extra dependencies.

## Quick start

after installation you can run below code to download market historical data:

```python 

from pycoin.data_gathering import KlineData_Fetcher
import datetime as dt

df = KlineData_Fetcher(symbol="BTC/USDT", 
                       timeframe="4h", 
                       data_exchange="binance",
                       since = dt.datetime(2020, 1, 1)
                       )

```

### ploting the candlestick data
```python

from pycoin.plotting import Market_Plotter

plots = Market_Plotter(OHLCV_df=df)

# if plot_by_grp is False then it will plot the whole candlestick data
figure = plots.plot_market()

# if plot_by_grp is True you can plot candlestick data by group and plot a specific year, month,etc.
figure = plots.plot_market(plot_by_grp=True, grp={"year":2023, "month":2})
figure.show()
```

![alt text](https://github.com/hadif1999/pycoin/blob/master/pics/btc_h4_2023.2_candlestick.png?raw=true)

### evaluating market high & lows
```python
from pycoin.data_gathering import get_market_High_Lows
df = get_market_High_Lows(df, candle_range = 100)
df                                                     
```
**candle_range** : range of candles to look for high and lows 
![alt text](https://github.com/hadif1999/pycoin/blob/master/pics/HighLow_df.png?raw=true)

### ploting market high and lows
```python

plots.plot_high_lows(df, R = 800, y_scale= 0.5)

```
![alt text](https://github.com/hadif1999/pycoin/blob/master/pics/btc_h4_HighLows_2020:2024.png?raw=true)

the method above puts a circle for each high and low. 
R is the radius and y_scale can scale the price in y axis for better visualizing.

### evaluate market trend with high and lows
every trend that is found with any method such as high & lows, SMA,etc. adds a new column that holds the trend label for each row of data, and when you want to plot these trend you should give this column name to draw_trend_highlight method.

```python
# finding trend 
from pycoin.data_gathering import Trend_Evaluator
df = Trend_Evaluator.eval_trend_with_high_lows(df, HighLow_range=100)

# ploting trend
plots.draw_trend_highlight("high_low_trend", df, 
                           add_high_lows_shapes = True,
                           R = 10000, # circle size of high and lows 
                           y_scale = 0.1 # scales y dim of circles 
                           )

```
![alt text](https://github.com/hadif1999/pycoin/blob/master/pics/btc_h4_2020:2023_trend.png?raw=true)
### evaluate trend using MACD + Signal
```python

df = Trend_Evaluator.eval_trend_with_MACD(df, drop_MACD_col = True)
plots.draw_trend_highlight("MACD_trend", df)

```
![alt text](https://github.com/hadif1999/pycoin/blob/master/pics/btc_h4_2020:2023_MACD_trend.png?raw=true)