### Run Example Script

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

Execute the example script to see the pytrendline library in action after cloning the repository and installing dependencies.

```bash
python example.py
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

Install all necessary libraries for the pytrendline project by running this command in your terminal.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install PyTrendline Library via Pip

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

Install the pytrendline library directly using pip for use in your Python projects.

```bash
pip install pytrendline
```

--------------------------------

### Override Default Algorithm Parameters

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

Customize trendline detection by passing a new configuration dictionary to the `detect` function. This example forces the maximum allowable error to be derived from the last candle's closing price.

```python
results = detect(
   …
   config={
        # Force the tolerance to be derived from last candle price
        "max_allowable_error_pt_to_trend": lambda candles: candles.df.iloc[-1].Close / 100,
   }
)
```

--------------------------------

### Default Scoring Function Example

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

This lambda function defines the default scoring mechanism for trendlines. It prioritizes trendlines with a low average distance to candle points and a higher number of detected points.

```python
"scoring_function": lambda candles, err_distances, num_points, slope: (util.avg_candle_range(candles) / util.mean(err_distances)) * (2.5 ** num_points),
```

--------------------------------

### Package Candlestick Data and Detect Trendlines

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

This snippet shows how to load OHLC data from a CSV, format it into a `CandlestickData` object, and then use `pytrendline.detect` to find trendlines. Configure detection parameters like trend type, pivot requirements, minimum points, and breakout behavior.

```python
# Package Candlestick Data
candles_df = pd.read_csv('./fixtures/example.csv')
candles_df['Date'] = pd.to_datetime(candles_df['Date'])

candlestick_data = pytrendline.CandlestickData(
  df=candles_df,
  time_interval="1m", # choose between 1m,3m,5m,10m,15m,30m,1h,1d
  open_col="Open", # name of the column containing candle "Open" price
  high_col="High", # name of the column containing candle "High" price
  low_col="Low", # name of the column containing candle "Low" price
  close_col="Close", # name of the column containing candle "Close" price
  datetime_col="Date" # name of the column containing candle datetime price (use none if datetime is in index)
)

# Detect
results = pytrendline.detect(
  candlestick_data=candlestick_data,

  # Choose between BOTH, SUPPORT or RESISTANCE
  trend_type=pytrendline.TrendlineTypes.BOTH,
  # Specify if you require the first point of a trendline to be a pivot
  first_pt_must_be_pivot=False,
  # Specify if you require the last point of the trendline to be a pivot
  last_pt_must_be_pivot=False,
  # Specify if you require all trendline points to be pivots
  all_pts_must_be_pivots=False,
  # Specify if you require one of the trendline points to be global max or min price
  trendline_must_include_global_maxmin_pt=False,
  # Specify minimum amount of points required for trendline detection (NOTE: must be at least two)
  min_points_required=3,
  # Specify if you want to ignore prices before some date
  scan_from_date=None,
  # Specify if you want to ignore 'breakout' lines. That is, lines that interesect a candle
  ignore_breakouts=True,
  # Specify and override to default config (See docs on how)
  config={}
)

```

--------------------------------

### Plot Trendline Detection Results

Source: https://github.com/ednunezg/pytrendline/blob/master/README.md

Visualize detected trendlines and pivot points using the `plot` function, which generates an interactive HTML chart. The output file is saved to the specified directory.

```python
outf = pytrendline.plot(
  results=results,
  filedir='.',
  filename='example_output.html',
)
os.system("open " + outf)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.