To compute the Commodity Channel Index (CCI) using Scala, you first need to calculate the typical price, which is the average of the high, low, and close prices of a security. Then, you calculate the mean deviation, which is the average of the absolute differences between the typical price and the simple moving average of the typical price over a specified period.

After calculating the mean deviation, you can compute the CCI using the formula:

CCI = (Typical Price - SMA of Typical Price) / (0.015 * Mean Deviation)

You can implement this algorithm in Scala by creating a function that takes the high, low, close prices, and the period as input parameters. Within the function, you can calculate the typical price, simple moving average, and mean deviation to finally compute the CCI value.

By following this process, you can effectively compute the Commodity Channel Index (CCI) using Scala for analyzing securities or other financial instruments.

## What is the significance of the period parameter in CCI calculation?

The period parameter in CCI (Commodity Channel Index) calculation is significant because it determines the number of periods used to calculate the indicator. The CCI measures the difference between the current price and its historical average price over a specified period. By changing the period parameter, traders can adjust the sensitivity of the CCI indicator to market movements. Shorter periods will produce more sensitivity to recent price changes, while longer periods will smooth out the indicator and reduce the noise. Traders can use the period parameter to tailor the CCI indicator to their specific trading strategy and time frame.

## What is the difference between simple CCI and modified CCI calculations?

The main difference between simple and modified CCI calculations lies in the way they are calculated.

- Simple CCI calculation:

**Simple CCI (Commodity Channel Index) is calculated using the formula**: CCI = (Typical Price - SMA of Typical Price) / (0.015 * Mean Deviation) Where: Typical Price = (High + Low + Close) / 3 SMA of Typical Price = Simple Moving Average of Typical Price over a specified period Mean Deviation = Mean Absolute Deviation of Typical Price over the same period

- Modified CCI calculation:

**Modified CCI is a variation of the standard CCI calculation that accounts for extreme price movements by using the median price instead of the typical price. The formula for modified CCI is**: CCI = (Median Price - SMA of Median Price) / (0.015 * Mean Deviation) Where: Median Price = (High + Low) / 2 SMA of Median Price = Simple Moving Average of Median Price over a specified period Mean Deviation = Mean Absolute Deviation of Median Price over the same period

In summary, the main difference between simple and modified CCI calculations is in the way they handle price data (typical vs. median) and how they calculate moving averages and mean absolute deviations based on that data. The modified CCI is often used to smooth out extreme price movements and provide a more accurate measure of market trends.

## How to plot CCI values over a time series in Scala?

To plot Commodity Channel Index (CCI) values over a time series in Scala, you can use a plotting library like "Breeze-viz". Here is an example code snippet demonstrating how to do this:

- First, add the following dependency to your build.sbt file:

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libraryDependencies += "org.scalanlp" %% "breeze" % "1.0" libraryDependencies += "org.scalanlp" %% "breeze-viz" % "1.0" |

- Import the necessary libraries in your Scala code:

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import breeze.plot._ import breeze.linalg._ import breeze.stats._ |

- Create a time series dataset with CCI values:

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val timeSeries = DenseVector(10.3, 13.1, 15.4, 12.7, 14.5) val cciValues = cci(timeSeries, 14) // Calculate CCI values for the time series with a period of 14 |

- Plot the CCI values over the time series:

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val fig = Figure() val plt = fig.subplot(0) plt += plot(DenseVector.range(0, timeSeries.length), timeSeries, name = "Time Series") plt += plot(DenseVector.range(0, timeSeries.length), cciValues, name = "CCI Values") plt.legend = true |

- Show the plot:

```
1
``` |
```
fig.refresh()
``` |

By running this code, you should be able to visualize the CCI values plotted over the time series in Scala using "Breeze-viz" library.

## How to detect overbought or oversold conditions using CCI in Scala?

To detect overbought or oversold conditions using the Commodity Channel Index (CCI) in Scala, you can create a function that calculates the CCI values and then determine when the CCI crosses certain threshold levels. Here is an example code snippet to achieve this in Scala:

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import scala.collection.mutable.ListBuffer def calculateCCI(prices: List[Double], periods: Int): List[Double] = { val typicalPrices = ListBuffer[Double]() prices.sliding(periods).foreach { period => val typicalPrice = (period.sum) / (period.length) typicalPrices += typicalPrice } val meanDeviation = ListBuffer[Double]() typicalPrices.sliding(periods).foreach { tp => val mean = tp.sum / tp.length val deviation = tp.map(t => Math.abs(t - mean)).sum / tp.length meanDeviation += deviation } val cci = ListBuffer[Double]() for (i <- 0 until typicalPrices.length) { cci += (typicalPrices(i) - (typicalPrices.take(i).sum / i)) / (0.015 * meanDeviation(i)) } cci.toList } def detectOverboughtOversold(cciValues: List[Double], overboughtThreshold: Double, oversoldThreshold: Double): List[String] = { cciValues.map { cci => if (cci > overboughtThreshold) "Overbought" else if (cci < oversoldThreshold) "Oversold" else "Normal" } } // Example usage val prices = List(100.0, 105.0, 110.0, 115.0, 120.0, 115.0, 110.0, 105.0, 100.0, 95.0) val cciValues = calculateCCI(prices, 5) val overboughtThreshold = 100.0 val oversoldThreshold = -100.0 val conditions = detectOverboughtOversold(cciValues, overboughtThreshold, oversoldThreshold) println(conditions) |

In this code snippet, the `calculateCCI`

function calculates the CCI values based on the typical price and mean deviation. The `detectOverboughtOversold`

function determines whether the CCI values are overbought, oversold, or normal based on user-defined threshold levels. You can adjust the threshold levels according to your specific requirements.

## How to handle outliers in CCI calculations in Scala?

In Scala, you can handle outliers in Commodity Channel Index (CCI) calculations by implementing the following steps:

**Identify outliers**: First, you need to identify outliers in your dataset. Outliers are data points that deviate significantly from the rest of the data and can skew CCI calculations.**Remove outliers**: Once you have identified outliers, you can choose to remove them from your dataset. You can do this by filtering out the outliers based on a predetermined threshold or using statistical methods such as z-score or interquartile range.**Impute outliers**: Instead of removing outliers, you can impute them by replacing them with a more representative value. You can use statistical techniques such as mean, median, or mode imputation to replace outliers with a more typical value.**Modify CCI calculation**: If you choose not to remove or impute outliers, you can modify the CCI calculation to be less sensitive to outliers. For example, you can use a robust CCI calculation method that is less affected by outliers.**Evaluate the impact**: Finally, evaluate the impact of handling outliers on your CCI calculations. Compare the results before and after handling outliers to determine if the changes improve the accuracy and reliability of your CCI analysis.

By following these steps, you can effectively handle outliers in CCI calculations in Scala and ensure more accurate and consistent results.