Quant.Strategy.MovingAverage (quant v0.1.0-alpha.1)

Moving average based trading strategies.

This module implements various moving average crossover strategies that generate buy/sell signals when faster moving averages cross above or below slower moving averages.

Supported Strategies

  • SMA Crossover: Simple Moving Average crossover
  • EMA Crossover: Exponential Moving Average crossover
  • Dual MA: Combination of different MA types

Strategy Examples

# Golden Cross: 50-day SMA crosses above 200-day SMA
strategy = Quant.Strategy.MovingAverage.sma_crossover(
  fast_period: 50,
  slow_period: 200
)

# Fast EMA crossover for short-term trading
strategy = Quant.Strategy.MovingAverage.ema_crossover(
  fast_period: 12,
  slow_period: 26
)

Summary

Functions

apply_indicators(dataframe, strategy, opts \\ [])

Apply the required technical indicators for moving average strategies.

ema_crossover(opts \\ [])

Create an Exponential Moving Average crossover strategy.

get_indicator_columns(strategy)

Get the column names that will be created by this strategy.

sma_crossover(opts \\ [])

Create a Simple Moving Average crossover strategy.

validate_dataframe(dataframe, strategy)

Validate that a DataFrame has the required columns for moving average strategies.

Functions

apply_indicators(dataframe, strategy, opts \\ [])

@spec apply_indicators(Explorer.DataFrame.t(), map(), keyword()) ::
  {:ok, Explorer.DataFrame.t()} | {:error, term()}

Apply the required technical indicators for moving average strategies.

This function adds the necessary moving averages to the DataFrame before signal generation.

Parameters

  • dataframe - Input DataFrame with OHLCV data
  • strategy - Strategy configuration
  • opts - Additional options

Returns

DataFrame with moving averages added as new columns.

ema_crossover(opts \\ [])

@spec ema_crossover(keyword()) :: map()

Create an Exponential Moving Average crossover strategy.

Parameters

  • :fast_period - Period for fast EMA (default: 12)
  • :slow_period - Period for slow EMA (default: 26)
  • :column - Price column to use (default: :close)

Examples

iex> strategy = Quant.Strategy.MovingAverage.ema_crossover(fast_period: 8, slow_period: 21)
iex> strategy.type
:ema_crossover
iex> strategy.indicator
:ema

get_indicator_columns(strategy)

@spec get_indicator_columns(map()) :: [String.t()]

Get the column names that will be created by this strategy.

Useful for understanding what columns will be added to the DataFrame.

Examples

iex> strategy = Quant.Strategy.MovingAverage.sma_crossover(fast_period: 5, slow_period: 10)
iex> Quant.Strategy.MovingAverage.get_indicator_columns(strategy)
["close_sma_5", "close_sma_10"]

sma_crossover(opts \\ [])

@spec sma_crossover(keyword()) :: map()

Create a Simple Moving Average crossover strategy.

Parameters

  • :fast_period - Period for fast SMA (default: 12)
  • :slow_period - Period for slow SMA (default: 26)
  • :column - Price column to use (default: :close)

Returns

Strategy configuration map for use with Quant.Strategy.generate_signals/2.

Examples

iex> strategy = Quant.Strategy.MovingAverage.sma_crossover(fast_period: 5, slow_period: 10)
iex> strategy.type
:sma_crossover
iex> strategy.fast_period
5
iex> strategy.slow_period
10

validate_dataframe(dataframe, strategy)

@spec validate_dataframe(Explorer.DataFrame.t(), map()) :: :ok | {:error, term()}

Validate that a DataFrame has the required columns for moving average strategies.

Parameters

  • dataframe - DataFrame to validate
  • strategy - Strategy configuration

Returns

:ok if valid, {:error, reason} if invalid.