Stochastic Oscillator

use centaur_technical_indicators::momentum_indicators::bulk::stochastic_oscillator;
use centaur_technical_indicators::chart_trends::{peaks, valleys};

fn proximity_rating(fuzzed_location: &usize, price_location: &usize) -> f64 {
    1.0 / (*fuzzed_location as f64 - *price_location as f64).abs()
}

pub fn main() {

    // fetch the data in your preferred way
    // let close = vec![...];  // closing prices

    // get buy and sell points, in an ideal world we would buy at the lowest point in the dip and sell at the highest point in the peak
    // In the course of a 20-day period (1 month of trading days), we want to find the highest peak and lowest valley within 5 days of each other
    let sell_points = peaks(&close, 20, 5).into_iter().map(|(_, i)| i).collect::<Vec<usize>>();
    let buy_points = valleys(&close, 20, 5).into_iter().map(|(_, i)| i).collect::<Vec<usize>>();

    // Define the ranges for optimization
    let max_period = 20;
    let min_period = 3;
    let min_oversold = 10;
    let max_oversold = 40;
    let min_overbought = 60;
    let max_overbought = 90;

    let fuzz_parameter = 5; // Allowable distance from buy/sell points

    // Store the best parameters found
    let mut best_rating = 0.0;
    let mut best_period = 0;
    let mut best_oversold = 0;
    let mut best_overbought = 0;
    let mut best_stochastics = vec![];

    for oversold in min_oversold..=max_oversold {
        for overbought in min_overbought..=max_overbought {
            for period in min_period..=max_period {
                let indicators = stochastic_oscillator(&close, period);
                let mut rating = vec![];
                let mut matched_sell = vec![];
                let mut matched_buy = vec![];
                for i in 0..indicators.len() {
                    let price_location = i + period;
                    if indicators[i] > overbought as f64 {
                        if sell_points.contains(&price_location) {
                            // If sell point == stochastic, rate positively
                            rating.push(1.0);
                            matched_sell.push(price_location);
                        } else if buy_points.contains(&price_location) {
                            // If buy point == stochastic, rate negatively
                            rating.push(-1.0);
                        } else {
                            let mut found_sell = false;
                            for fuzzed_location in (price_location - fuzz_parameter)..=(price_location + fuzz_parameter) {
                                // It's ok if we count multiple times for fuzzed locations as we reduce the rating
                                // based off of distance from the actual sell point which will impact the final rating
                                if sell_points.contains(&fuzzed_location) {
                                    rating.push(proximity_rating(&fuzzed_location, &price_location));
                                    matched_sell.push(fuzzed_location);
                                    found_sell = true;
                                }
                                if buy_points.contains(&fuzzed_location) {
                                    // Note the `-` here to penalize for selling instead of buying
                                    if !matched_sell.contains(&fuzzed_location) {
                                        rating.push(-proximity_rating(&fuzzed_location, &price_location));
                                    }
                                }
                            }
                            if !found_sell {
                                rating.push(0.0);
                            }
                        }
                    } else if indicators[i] < oversold as f64 {
                        if buy_points.contains(&price_location) {
                            // If buy point == stochastic, rate positively
                            rating.push(1.0);
                            matched_buy.push(price_location);
                        } else if sell_points.contains(&price_location) {
                            rating.push(-1.0);
                        } else {
                            let mut found_buy = false;
                            for fuzzed_location in (price_location - fuzz_parameter)..=(price_location + fuzz_parameter) {
                                // It's ok if we count multiple times for fuzzed locations as we reduce the rating
                                // based off of distance from the actual sell point which will impact the final rating
                                if buy_points.contains(&fuzzed_location) {
                                    rating.push(proximity_rating(&fuzzed_location, &price_location));
                                    matched_buy.push(fuzzed_location);
                                    found_buy = true;
                                }
                                if sell_points.contains(&fuzzed_location) {
                                    // Note the `-` here to penalize for buying instead of selling
                                    if !matched_buy.contains(&fuzzed_location) {
                                        rating.push(-proximity_rating(&fuzzed_location, &price_location));
                                    }
                                }
                            }
                            if !found_buy {
                                rating.push(0.0);
                            }
                        }
                    }
                }
                // Look for any missed buy/sell points and penalize
                for missed_sell in sell_points.iter() {
                    if !matched_sell.contains(missed_sell) {
                        rating.push(-1.0);
                    }
                }
                for missed_buy in buy_points.iter() {
                    if !matched_buy.contains(missed_buy) {
                        rating.push(-1.0);
                    }
                }
                let total_rating: f64 = rating.iter().sum::<f64>() / (rating.len() as f64);
                if total_rating > best_rating {
                    best_rating = total_rating;
                    best_period = period;
                    best_oversold = oversold;
                    best_overbought = overbought;
                    best_stochastics = indicators.clone();
                }
            }
        }
    }

    println!("Best Stochastic parameters found:");
    println!("Period: {}", best_period);
    println!("Oversold threshold: {}", best_oversold);
    println!("Overbought threshold: {}", best_overbought);
    println!("Rating: {}", best_rating);
    println!("Best Stochastic values: {:?}", best_stochastics);

}