Williams %R

use centaur_technical_indicators::momentum_indicators::bulk::{williams_percent_r};
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

    let indicator_loop = Instant::now();

  // 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 = 2;
    let min_oversold = 50;
    let max_oversold = 100;
    let min_overbought = 0;
    let max_overbought = 50;

    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_indicators = vec![];

    for oversold in min_oversold..=max_oversold {
        let oversold = oversold as f64 * -1.0; // Williams %R is negative
        for overbought in min_overbought..=max_overbought {
            let overbought = overbought as f64 * -1.0; // Williams %R is negative
            for period in min_period..=max_period {
                    let indicators = williams_percent_r(&high, &low, &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 == Williams %R, rate positively
                                rating.push(1.0);
                                matched_sell.push(price_location);
                            } else if buy_points.contains(&price_location) {
                                // If buy point == Williams %R, 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 == Williams %R, 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_model = ma_type;
                        best_oversold = (oversold * -1.0) as usize;
                        best_overbought = (overbought * -1.0) as usize;
                        best_indicators = indicators.clone();
                    }
                }
            }
    }

    println!(
        "Indicators optimization loop took {} ms to run",
        indicator_loop.elapsed().as_millis()
    );

    println!("
Best Williams %R parameters found:");
    println!("Period: {}", best_period);
    println!("Oversold threshold: {}", best_oversold as f64 * -1.0);
    println!("Overbought threshold: {}", best_overbought as f64 * -1.0);
    println!("Rating: {}", best_rating);
    println!("Best Williams %R values: {:?}", best_indicators);
}