https://github.com/rafa-rod/pytrendseries

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.
https://github.com/rafa-rod/pytrendseries

downtrend drawdown drawdown-at-risk finance maximum-drawdown-at-risk python quant quantitative-finance time-series time-series-analysis time-under-water timeseries timeunderwater trends-detected uptrend

Last synced: 2 months ago
JSON representation

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.

• Host: GitHub
• URL: https://github.com/rafa-rod/pytrendseries
• Owner: rafa-rod
• Created: 2021-05-17T01:17:03.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-01-09T20:23:16.000Z (6 months ago)
• Last Synced: 2024-03-29T23:22:43.419Z (3 months ago)
• Topics: downtrend, drawdown, drawdown-at-risk, finance, maximum-drawdown-at-risk, python, quant, quantitative-finance, time-series, time-series-analysis, time-under-water, timeseries, timeunderwater, trends-detected, uptrend
• Language: Python
• Homepage:
• Size: 5.5 MB
• Stars: 95
• Watchers: 2
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Lists

• awesome-quant - pytrendseries - Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater. (Python / Trading & Backtesting)
• awesome-stars - rafa-rod/pytrendseries - Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater. (Python)

README

        



    

          

    

          

      

        

      



**pytrendseries** is a Python library for detection of trends in time series like: stock prices, monthly sales, daily temperature of a city and so on.

The input data must be a `pandas.DataFrame` format containing one column as observed data (in float or int format). Follow example below:

```python

import pandas as pd

data = pd.read_csv("tests/resource/stock_prices.csv")

filtered_data = data[['period','close']].set_index("period")

filtered_data.columns = ['close_price']

filtered_data.index = pd.to_datetime(filtered_data.index)

filtered_data = filtered_data.sort_index()

```

Once some trend is identified, **pytrendseries** provides period on trend, drawdown, maximum drawdown (or drawup in case of uptrend) and a plot with all trends found.

## Installation

### Using pip

You can install using the pip package manager by running:

```sh

pip install pytrendseries

```

Alternatively, you could install the latest version directly from Github:

```sh

pip install https://github.com/rafa-rod/pytrendseries/archive/refs/heads/main.zip

```

## Why pytrendseries is important?

Detection of trends could be used in machine learning algorithms such as classification problems like binary (1 = uptrend, 0 = otherwise) or non-binary classifications (1 = uptrend, -1 = downtrend, 0 = otherwise). Besides that, could be used in prediction problems.

## Example

Inform:

 - type of trend you desire to investigate => downtrend or uptrend;

 - window or maximum period of a trend (example: 60 days considering 1 day as 1 period);

 - the minimum value that represents the number of consecutive days (or another period of time) to be considered a trend (default 5 periods).

```python

import pytrendseries

trend = "downtrend"

window = 126 #6 months

trends_detected = pytrendseries.detecttrend(filtered_data, trend=trend, window=window)

```

The variable `trends_detected` is a dataframe that contains the initial and end date of each trend, the prices of each date, time span of each trend and the drawdown of each trend. Let's see the first five rows of this dataframe:

```

| from                | to                  |   price0 |   price1 |   indice_from |   indice_to |   time_span |   drawdown |

|:--------------------|:--------------------|---------:|---------:|--------------:|------------:|------------:|-----------:|

| 2000-01-03 00:00:00 | 2000-01-31 00:00:00 |  5.90057 |  5.12252 |             0 |          19 |          19 |  0.131859  |

| 2000-03-09 00:00:00 | 2000-04-24 00:00:00 |  6.42701 |  5.02208 |            45 |          76 |          31 |  0.218597  |

| 2000-05-02 00:00:00 | 2000-05-11 00:00:00 |  5.53684 |  5.29352 |            81 |          88 |           7 |  0.0439456 |

| 2000-05-16 00:00:00 | 2000-05-24 00:00:00 |  5.59962 |  5.24807 |            91 |          97 |           6 |  0.0627803 |

| 2000-06-08 00:00:00 | 2000-06-15 00:00:00 |  6.30359 |  6.1646  |           108 |         113 |           5 |  0.0220487 |

```

The easiest way to vizualize the trends detected, just call `plot_trend` function.

All trends detected, with maximum window informed and the minimum informed by the limit value, will be displayed.

```python

import pytrendseries

trend = "downtrend"

window = 30

year = 2020

trends_detected = pytrendseries.detecttrend(filtered_data, trend=trend, window=window)

pytrendseries.vizplot.plot_trend(filtered_data, trends_detected, trend, year)

```



To visualize all uptrends found, inform `trend='uptrend'`:

```python

import pytrendseries

window = 30

year = 2020

trends_detected = pytrendseries.detecttrend(filtered_data, trend='uptrend', window=window)

pytrendseries.vizplot.plot_trend(filtered_data, trends_detected, 'uptrend', year)

```

 



The maximum drawdown or maximum drawup can be obtained by sorting the dataframe by column drawdown. To do that, just code:

```python

maxdd_in_window = trends_detected.sort_values("drawdown", ascending=False).iloc[0:1]

```

Another way is to call the function `maxdrawdown`. Note that this result will be differente once the maximum drawdown of the intire timeseries.

```python

maxdd = pytrendseries.maxdrawdown(filtered_data)

```

You can code to vizualize as follows:

```python

import matplotlib.pyplot as plt

plt.figure(figsize=(14,5))

plt.plot(filtered_data, alpha=0.6)

location_x = maxdd.values[:,0]

location_y = maxdd.values[:,1]

for i in range(location_x.shape[0]):

    plt.axvspan(location_x[i], location_y[i], alpha=0.3, color="red")

plt.grid(axis='x')

plt.show()

```



You may pass the parameter window to obtain the same result:

```python

maxdd_in_window = pytrendseries.maxdrawdown(filtered_data, window=252)

```

To vizualize all drawdowns of timeseries, call the following function:

```python

import pytrendseries

pytrendseries.plot_drawdowns(filtered_data, figsize = (10,4), color="gray", alpha=0.6, title="Drawdowns", axis="y")

```



Another option is:

```python

import pytrendseries

pytrendseries.plot_evolution(filtered_data, figsize = (10,4), colors=["gray", "red"], alphas=[1,0.6])

```



To get time underwater (tuw), just type:

```python

import pytrendseries

pytrendseries.tuw(filtered_data)

```

The output would be (showing the tail of the dataframe):

```

| inital_date|   peak    |   valley  |   drawdown  |   time underwater |   final_date |

|:-----------|----------:|----------:|------------:|------------------:|-------------:|

| 2007-12-28 |  44.66140 |  33.58194 |     0.24808 |                85 |   2008-05-06 |

| 2008-05-06 |  45.00000 |  44.85000 |     0.00333 |                4  |   2008-05-09 |

| 2008-05-13 |  46.95000 |  46.30000 |     0.01384 |                3  |   2008-05-15 |

| 2008-05-21 |  52.51000 |  4.20000  |     0.92002 |               NaN |          NaN |

```

The table shows time underwater as NaN, it means that the timeseries still on downtrend.

Another important usage of `pytrendseries` is to obtain the series of drawdowns or series of maximum drawdowns in order to calculate the drawdown at risk or maximum drawdown at risk.

```python

import pytrendseries

import matplotlib.pyplot as plt

import seaborn as sns; sns.set_style("white")

trend = "downtrend"

window = 126 #6 months

trends_detected = pytrendseries.detecttrend(filtered_data, trend=trend, window=window)

plt.figure(figsize=(15,5))

sns.histplot(trends_detected["drawdown"]*100, kde=True, bins=30)

plt.ylabel("")

plt.box(False)

plt.annotate('Maximum Drawdown', xy=((trends_detected["drawdown"].max()-0.005)*100, 1),

             xycoords='data',

            xytext=(-105, 30), textcoords='offset points',color="red",

            weight='bold',

            arrowprops=dict(arrowstyle="->", color="r",

                            connectionstyle='arc3,rad=-0.1'))

plt.annotate('Quantile 97,5%', xy=((trends_detected["drawdown"].quantile(0.975)-0.005)*100, 0.2),

             xycoords='data',

            xytext=(-135, 30), textcoords='offset points',color="red",

            weight='bold',

            arrowprops=dict(arrowstyle="->", color="r",

                            connectionstyle='arc3,rad=-0.1'))

plt.xlabel("Drawdown (%)")

plt.ylabel("Density", rotation=0, labelpad=-30, loc="top")

plt.show()

```



```python

import pytrendseries

import matplotlib.pyplot as plt

import seaborn as sns; sns.set_style("white")

maxdd_in_window = maxdrawdown(filtered_data, window=126)

plt.figure(figsize=(15,5))

sns.histplot(maxdd_in_window["MaxDD"]*100, kde=True, bins=30)

plt.ylabel("")

plt.box(False)

plt.annotate('Maximum Drawdown', xy=((maxdd_in_window["MaxDD"].max()-0.005)*100, 1),

             xycoords='data',

            xytext=(-105, 30), textcoords='offset points',color="red",

            weight='bold',

            arrowprops=dict(arrowstyle="->", color="r",

                            connectionstyle='arc3,rad=-0.1'))

plt.annotate('Quantile 95%', xy=((maxdd_in_window["MaxDD"].quantile(0.95)-0.005)*100, 0.2),

             xycoords='data',

            xytext=(-135, 50), textcoords='offset points',color="red",

            weight='bold',

            arrowprops=dict(arrowstyle="->", color="r",

                            connectionstyle='arc3,rad=-0.1'))

plt.xlabel("Maximum Drawdowns (%)")

plt.ylabel("Density", rotation=0, labelpad=-30, loc="top")

plt.show()

```