# Moving Average Convergence / Divergence (MACD)

get_macd(quotes, fast_periods=12, slow_periods=26, signal_periods=9)

## Parameters

name | type | notes |
---|---|---|

`quotes` | Iterable[Quote] | Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame |

`fast_periods` | int, default 12 | Number of periods (`F` ) for the faster moving average. Must be greater than 0. |

`slow_periods` | int, default 26 | Number of periods (`S` ) for the slower moving average. Must be greater than `fast_periods` . |

`signal_periods` | int, default 9 | Number of periods (`P` ) for the moving average of MACD. Must be greater than or equal to 0. |

`candle_part` | CandlePart, default CandlePart.CLOSE | Specify candle part to evaluate. See CandlePart options below. |

### Historical quotes requirements

You must have at least `2×(S+P)`

or `S+P+100`

worth of `quotes`

, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least `S+P+250`

data points prior to the intended usage date for better precision.

`quotes`

is an `Iterable[Quote]`

collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

#### CandlePart options

```
from stock_indicators.indicators.common.enums import CandlePart
```

type | description |
---|---|

`CandlePart.OPEN` | `open` price |

`CandlePart.HIGH` | `high` price |

`CandlePart.LOW` | `low` price |

`CandlePart.CLOSE` | `close` price |

`CandlePart.VOLUME` | `volume` |

`CandlePart.HL2` | `(high+low)/2` |

`CandlePart.HLC3` | `(high+low+close)/3` |

`CandlePart.OC2` | `(open+close)/2` |

`CandlePart.OHL3` | `(open+high+low)/3` |

`CandlePart.OHLC4` | `(open+high+low+close)/4` |

## Returns

```
MACDResults[MACDResult]
```

- This method returns a time series of all available indicator values for the
`quotes`

provided. `MACDResults`

is just a list of`MACDResult`

.- It always returns the same number of elements as there are in the historical quotes.
- It does not return a single incremental indicator value.
- The first
`S-1`

slow periods will have`None`

values since there’s not enough data to calculate.

⚞

Convergence warning: The first`S+P+250`

periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

### MACDResult

name | type | notes |
---|---|---|

`date` | datetime | Date |

`macd` | float, Optional | The MACD line is the difference between slow and fast moving averages (`macd = fast_ema - slow_ema` ) |

`signal` | float, Optional | Moving average of the `macd` line |

`histogram` | float, Optional | Gap between of the `macd` and `signal` line |

`fast_ema` | float, Optional | Fast Exponential Moving Average |

`slow_ema` | float, Optional | Slow Exponential Moving Average |

### Utilities

See Utilities and Helpers for more information.

## Example

```
from stock_indicators import indicators
# This method is NOT a part of the library.
quotes = get_history_from_feed("SPY")
# calculate MACD(12,26,9)
results = indicators.get_macd(quotes, 12, 26, 9)
```

## About Moving Average Convergence / Divergence (MACD)

Created by Gerald Appel, MACD is a simple oscillator view of two converging/diverging exponential moving averages. [Discuss] 💬