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# Commodity Channel Index (CCI) In Haskell?

The Commodity Channel Index (CCI) is a popular technical indicator used in financial markets to identify overbought or oversold conditions. It measures the difference between an asset's current price and its average price over a specified period of time.

In Haskell, the CCI can be implemented by first calculating the typical price, which is the average of the high, low, and close prices for each period. Then, the mean deviation is calculated as the average of the absolute differences between the typical price and the simple moving average of the typical price over the same period.

Finally, the CCI value is calculated as the difference between the typical price and the simple moving average of the typical price, divided by 0.015 times the mean deviation. A CCI value above +100 indicates that the asset is overbought, while a value below -100 indicates oversold conditions.

Overall, implementing the Commodity Channel Index in Haskell involves calculating various moving averages and typical prices to determine the current market conditions of an asset.

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## How to combine CCI with other technical indicators in Haskell?

Combining the Commodity Channel Index (CCI) with other technical indicators in Haskell can help to provide a more comprehensive analysis of the market. Here's an example of how you can combine CCI with another technical indicator, such as the Moving Average Convergence Divergence (MACD), in Haskell:

1. Define functions to calculate CCI and MACD:
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 ``` ```-- CCI calculation function cci :: [Double] -> Int -> Int -> [Double] cci prices n period = map calculateCCI [n-1..length prices - 1] where calculateCCI i = (typicalPrice - sma) / (0.015 * meanDeviation) where typicalPrice = (high + low + close) / 3 high = maximum (take period \$ drop (i-n+1) prices) low = minimum (take period \$ drop (i-n+1) prices) close = prices !! i sma = sum (take n \$ drop i prices) / fromIntegral n meanDeviation = sum (map (\x -> abs (x - sma)) (take n \$ drop i prices)) / fromIntegral n -- MACD calculation function macd :: [Double] -> Double -> Double -> Double -> ([Double], [Double]) macd prices shortPeriod longPeriod signalPeriod = (macdLine, signalLine) where shortEMA = ema prices shortPeriod longEMA = ema prices longPeriod macdLine = zipWith (-) shortEMA longEMA signalLine = ema macdLine signalPeriod ```

1. Use the calculated CCI and MACD values to make trading decisions:
 ```1 2 3 4 5 6 7 8 ``` ```main :: IO () main = do let prices = [100.0, 105.0, 110.0, 115.0, 120.0, 125.0, 130.0, 135.0, 140.0] cciValues = cci prices 5 5 (macdLine, signalLine) = macd prices 12 26 9 print cciValues print macdLine print signalLine ```

In this example, we first calculate the CCI values and MACD values for a given set of prices. Then, we can use these values to analyze the market trends and make trading decisions based on the signals provided by the indicators.

You can further extend this code by combining more technical indicators, adding trading strategies, or implementing backtesting to validate the effectiveness of the combined indicators.

## What is the default period setting for CCI in Haskell?

The default period setting for the CCI (Commodity Channel Index) in Haskell is typically set to 14.

## How to plot CCI on a chart in Haskell?

To plot the Commodity Channel Index (CCI) on a chart in Haskell, you can use the Chart library. Here is an example code to plot CCI on a chart:

 ``` 1 2 3 4 5 6 7 8 9 10 ``` ```import Graphics.Rendering.Chart.Easy import Graphics.Rendering.Chart.Backend.Cairo cciData :: [(Double, Double)] cciData = [(1, 100), (2, 105), (3, 110), (4, 95), (5, 85)] -- Replace with your own CCI data main :: IO () main = toFile def "cci_chart.png" \$ do layout_title .= "CCI Chart" plot \$ line "CCI" [cciData] ```

In this code, `cciData` contains the data points for CCI values. Replace this with your actual CCI data. Then, the `main` function creates a new chart with title "CCI Chart" and plots the CCI data using a line chart. Finally, it saves the chart as a PNG file named "cci_chart.png".

You can customize the chart further by changing the layout settings or adding additional plots as needed. Make sure to install the required Chart library using cabal before running the code.

## How to customize the period setting for CCI in Haskell?

To customize the period setting for CCI (Commodity Channel Index) in Haskell, you can create a function that calculates the CCI value with a specific period length. Here is an example implementation:

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ``` ```import Data.List cci :: Int -> [Double] -> [Double] -> [Double] cci period highs lows closes = map calculateCCI cciData where typicalPrices = map (\(h, l, c) -> (h + l + c) / 3) \$ zip3 highs lows closes meanDeviation = map (calculateMeanDeviation period typicalPrices) [0..length typicalPrices - period] cciData = zip typicalPrices meanDeviation calculateMeanDeviation :: Int -> [Double] -> Int -> Double calculateMeanDeviation period typicalPrices idx = sum deviations / fromIntegral period where slice = take period \$ drop idx typicalPrices meanPrice = sum slice / fromIntegral period deviations = map (\price -> abs(price - meanPrice)) slice calculateCCI :: (Double, Double) -> Double calculateCCI (typicalPrice, meanDeviation) = (typicalPrice - mean) / (factor * meanDeviation) where mean = sum (map fst cciData) / fromIntegral (length cciData) factor = 0.015 -- You can customize this factor as needed -- Example usage main :: IO () main = do let highs = [10.0, 15.0, 20.0, 25.0] lows = [8.0, 12.0, 18.0, 22.0] closes = [9.0, 14.0, 19.0, 24.0] period = 2 -- Customize the period setting here let cciValues = cci period highs lows closes putStrLn \$ "CCI values: " ++ show cciValues ```

In this implementation, the `cci` function calculates the CCI values for a given period setting using typical prices calculated from the high, low, and close prices provided. The `calculateCCI` function calculates the CCI value for each data point based on the typical price and mean deviation. You can customize the period setting by changing the `period` variable in the `main` function.

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