1000 lines
33 KiB
JavaScript
1000 lines
33 KiB
JavaScript
const PATTERN = {
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FLUCTUATING: 0,
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LARGE_SPIKE: 1,
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DECREASING: 2,
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SMALL_SPIKE: 3,
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};
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const PROBABILITY_MATRIX = {
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[PATTERN.FLUCTUATING]: {
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[PATTERN.FLUCTUATING]: 0.20,
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[PATTERN.LARGE_SPIKE]: 0.30,
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[PATTERN.DECREASING]: 0.15,
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[PATTERN.SMALL_SPIKE]: 0.35,
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},
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[PATTERN.LARGE_SPIKE]: {
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[PATTERN.FLUCTUATING]: 0.50,
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[PATTERN.LARGE_SPIKE]: 0.05,
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[PATTERN.DECREASING]: 0.20,
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[PATTERN.SMALL_SPIKE]: 0.25,
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},
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[PATTERN.DECREASING]: {
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[PATTERN.FLUCTUATING]: 0.25,
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[PATTERN.LARGE_SPIKE]: 0.45,
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[PATTERN.DECREASING]: 0.05,
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[PATTERN.SMALL_SPIKE]: 0.25,
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},
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[PATTERN.SMALL_SPIKE]: {
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[PATTERN.FLUCTUATING]: 0.45,
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[PATTERN.LARGE_SPIKE]: 0.25,
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[PATTERN.DECREASING]: 0.15,
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[PATTERN.SMALL_SPIKE]: 0.15,
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},
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};
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const RATE_MULTIPLIER = 10000;
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function range_length(range) {
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return range[1] - range[0];
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}
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function clamp(x, min, max) {
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return Math.min(Math.max(x, min), max);
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}
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function range_intersect(range1, range2) {
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if (range1[0] > range2[1] || range1[1] < range2[0]) {
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return null;
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}
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return [Math.max(range1[0], range2[0]), Math.min(range1[1], range2[1])];
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}
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function range_intersect_length(range1, range2) {
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if (range1[0] > range2[1] || range1[1] < range2[0]) {
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return 0;
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}
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return range_length(range_intersect(range1, range2));
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}
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/**
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* Accurately sums a list of floating point numbers.
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* See https://en.wikipedia.org/wiki/Kahan_summation_algorithm#Further_enhancements
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* for more information.
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* @param {number[]} input
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* @returns {number} The sum of the input.
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*/
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function float_sum(input) {
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// Uses the improved Kahan–Babuska algorithm introduced by Neumaier.
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let sum = 0;
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// The "lost bits" of sum.
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let c = 0;
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for (let i = 0; i < input.length; i++) {
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const cur = input[i];
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const t = sum + cur;
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if (Math.abs(sum) >= Math.abs(cur)) {
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c += (sum - t) + cur;
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} else {
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c += (cur - t) + sum;
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}
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sum = t;
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}
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return sum + c;
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}
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/**
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* Accurately returns the prefix sum of a list of floating point numbers.
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* See https://en.wikipedia.org/wiki/Kahan_summation_algorithm#Further_enhancements
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* for more information.
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* @param {number[]} input
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* @returns {[number, number][]} The prefix sum of the input, such that
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* output[i] = [sum of first i integers, error of the sum].
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* The "true" prefix sum is equal to the sum of the pair of numbers, but it is
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* explicitly returned as a pair of numbers to ensure that the error portion
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* isn't lost when subtracting prefix sums.
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*/
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function prefix_float_sum(input) {
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const prefix_sum = [[0, 0]];
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let sum = 0;
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let c = 0;
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for (let i = 0; i < input.length; i++) {
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const cur = input[i];
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const t = sum + cur;
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if (Math.abs(sum) >= Math.abs(cur)) {
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c += (sum - t) + cur;
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} else {
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c += (cur - t) + sum;
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}
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sum = t;
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prefix_sum.push([sum, c]);
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}
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return prefix_sum;
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}
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/*
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* Probability Density Function of rates.
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* Since the PDF is continuous*, we approximate it by a discrete probability function:
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* the value in range [x, x + 1) has a uniform probability
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* prob[x - value_start];
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*
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* Note that we operate all rate on the (* RATE_MULTIPLIER) scale.
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*
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* (*): Well not really since it only takes values that "float" can represent in some form, but the
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* space is too large to compute directly in JS.
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*/
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class PDF {
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/**
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* Initialize a PDF in range [a, b], a and b can be non-integer.
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* if uniform is true, then initialize the probability to be uniform, else initialize to a
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* all-zero (invalid) PDF.
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* @param {number} a - Left end-point.
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* @param {number} b - Right end-point end-point.
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* @param {boolean} uniform - If true, initialise with the uniform distribution.
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*/
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constructor(a, b, uniform = true) {
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// We need to ensure that [a, b] is fully contained in [value_start, value_end].
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/** @type {number} */
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this.value_start = Math.floor(a);
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/** @type {number} */
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this.value_end = Math.ceil(b);
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const range = [a, b];
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const total_length = range_length(range);
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/** @type {number[]} */
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this.prob = Array(this.value_end - this.value_start);
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if (uniform) {
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for (let i = 0; i < this.prob.length; i++) {
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this.prob[i] =
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range_intersect_length(this.range_of(i), range) / total_length;
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}
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}
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}
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/**
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* Calculates the interval represented by this.prob[idx]
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* @param {number} idx - The index of this.prob
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* @returns {[number, number]} The interval representing this.prob[idx].
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*/
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range_of(idx) {
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// We intentionally include the right end-point of the range.
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// The probability of getting exactly an endpoint is zero, so we can assume
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// the "probability ranges" are "touching".
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return [this.value_start + idx, this.value_start + idx + 1];
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}
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min_value() {
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return this.value_start;
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}
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max_value() {
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return this.value_end;
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}
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/**
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* @returns {number} The sum of probabilities before normalisation.
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*/
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normalize() {
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const total_probability = float_sum(this.prob);
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for (let i = 0; i < this.prob.length; i++) {
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this.prob[i] /= total_probability;
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}
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return total_probability;
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}
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/*
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* Limit the values to be in the range, and return the probability that the value was in this
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* range.
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*/
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range_limit(range) {
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let [start, end] = range;
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start = Math.max(start, this.min_value());
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end = Math.min(end, this.max_value());
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if (start >= end) {
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// Set this to invalid values
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this.value_start = this.value_end = 0;
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this.prob = [];
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return 0;
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}
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start = Math.floor(start);
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end = Math.ceil(end);
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const start_idx = start - this.value_start;
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const end_idx = end - this.value_start;
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for (let i = start_idx; i < end_idx; i++) {
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this.prob[i] *= range_intersect_length(this.range_of(i), range);
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}
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this.prob = this.prob.slice(start_idx, end_idx);
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this.value_start = start;
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this.value_end = end;
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// The probability that the value was in this range is equal to the total
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// sum of "un-normalised" values in the range.
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return this.normalize();
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}
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/**
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* Subtract the PDF by a uniform distribution in [rate_decay_min, rate_decay_max]
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*
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* For simplicity, we assume that rate_decay_min and rate_decay_max are both integers.
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* @param {number} rate_decay_min
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* @param {number} rate_decay_max
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* @returns {void}
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*/
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decay(rate_decay_min, rate_decay_max) {
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// In case the arguments aren't integers, round them to the nearest integer.
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rate_decay_min = Math.round(rate_decay_min);
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rate_decay_max = Math.round(rate_decay_max);
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// The sum of this distribution with a uniform distribution.
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// Let's assume that both distributions start at 0 and X = this dist,
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// Y = uniform dist, and Z = X + Y.
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// Let's also assume that X is a "piecewise uniform" distribution, so
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// x(i) = this.prob[Math.floor(i)] - which matches our implementation.
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// We also know that y(i) = 1 / max(Y) - as we assume that min(Y) = 0.
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// In the end, we're interested in:
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// Pr(i <= Z < i+1) where i is an integer
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// = int. x(val) * Pr(i-val <= Y < i-val+1) dval from 0 to max(X)
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// = int. x(floor(val)) * Pr(i-val <= Y < i-val+1) dval from 0 to max(X)
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// = sum val from 0 to max(X)-1
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// x(val) * f_i(val) / max(Y)
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// where f_i(val) =
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// 0.5 if i-val = 0 or max(Y), so val = i-max(Y) or i
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// 1.0 if 0 < i-val < max(Y), so i-max(Y) < val < i
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// as x(val) is "constant" for each integer step, so we can consider the
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// integral in integer steps.
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// = sum val from max(0, i-max(Y)) to min(max(X)-1, i)
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// x(val) * f_i(val) / max(Y)
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// for example, max(X)=1, max(Y)=10, i=5
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// = sum val from max(0, 5-10)=0 to min(1-1, 5)=0
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// x(val) * f_i(val) / max(Y)
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// = x(0) * 1 / 10
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// Get a prefix sum / CDF of this so we can calculate sums in O(1).
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const prefix = prefix_float_sum(this.prob);
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const max_X = this.prob.length;
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const max_Y = rate_decay_max - rate_decay_min;
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const newProb = Array(this.prob.length + max_Y);
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for (let i = 0; i < newProb.length; i++) {
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// Note that left and right here are INCLUSIVE.
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const left = Math.max(0, i - max_Y);
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const right = Math.min(max_X - 1, i);
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// We want to sum, in total, prefix[right+1], -prefix[left], and subtract
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// the 0.5s if necessary.
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// This may involve numbers of differing magnitudes, so use the float sum
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// algorithm to sum these up.
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const numbers_to_sum = [
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prefix[right + 1][0], prefix[right + 1][1],
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-prefix[left][0], -prefix[left][1],
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];
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if (left === i-max_Y) {
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// Need to halve the left endpoint.
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numbers_to_sum.push(-this.prob[left] / 2);
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}
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if (right === i) {
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// Need to halve the right endpoint.
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// It's guaranteed that we won't accidentally "halve" twice,
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// as that would require i-max_Y = i, so max_Y = 0 - which is
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// impossible.
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numbers_to_sum.push(-this.prob[right] / 2);
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}
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newProb[i] = float_sum(numbers_to_sum) / max_Y;
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}
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this.prob = newProb;
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this.value_start -= rate_decay_max;
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this.value_end -= rate_decay_min;
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// No need to normalise, as it is guaranteed that the sum of this.prob is 1.
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}
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}
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class Predictor {
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constructor(prices, first_buy, previous_pattern) {
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// The reverse-engineered code is not perfectly accurate, especially as it's not
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// 32-bit ARM floating point. So, be tolerant of slightly unexpected inputs
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this.fudge_factor = 0;
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this.prices = prices;
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this.first_buy = first_buy;
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this.previous_pattern = previous_pattern;
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}
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intceil(val) {
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return Math.trunc(val + 0.99999);
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}
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minimum_rate_from_given_and_base(given_price, buy_price) {
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return RATE_MULTIPLIER * (given_price - 0.99999) / buy_price;
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}
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maximum_rate_from_given_and_base(given_price, buy_price) {
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return RATE_MULTIPLIER * (given_price + 0.00001) / buy_price;
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}
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rate_range_from_given_and_base(given_price, buy_price) {
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return [
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this.minimum_rate_from_given_and_base(given_price, buy_price),
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this.maximum_rate_from_given_and_base(given_price, buy_price)
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];
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}
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get_price(rate, basePrice) {
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return this.intceil(rate * basePrice / RATE_MULTIPLIER);
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}
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* multiply_generator_probability(generator, probability) {
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for (const it of generator) {
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yield {...it, probability: it.probability * probability};
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}
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}
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/*
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* This corresponds to the code:
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* for (int i = start; i < start + length; i++)
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* {
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* sellPrices[work++] =
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* intceil(randfloat(rate_min / RATE_MULTIPLIER, rate_max / RATE_MULTIPLIER) * basePrice);
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* }
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*
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* Would return the conditional probability given the given_prices, and modify
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* the predicted_prices array.
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* If the given_prices won't match, returns 0.
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*/
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generate_individual_random_price(
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given_prices, predicted_prices, start, length, rate_min, rate_max) {
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rate_min *= RATE_MULTIPLIER;
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rate_max *= RATE_MULTIPLIER;
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const buy_price = given_prices[0];
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const rate_range = [rate_min, rate_max];
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let prob = 1;
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for (let i = start; i < start + length; i++) {
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let min_pred = this.get_price(rate_min, buy_price);
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let max_pred = this.get_price(rate_max, buy_price);
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if (!isNaN(given_prices[i])) {
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if (given_prices[i] < min_pred - this.fudge_factor || given_prices[i] > max_pred + this.fudge_factor) {
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// Given price is out of predicted range, so this is the wrong pattern
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return 0;
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}
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// TODO: How to deal with probability when there's fudge factor?
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// Clamp the value to be in range now so the probability won't be totally biased to fudged values.
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const real_rate_range =
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this.rate_range_from_given_and_base(clamp(given_prices[i], min_pred, max_pred), buy_price);
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prob *= range_intersect_length(rate_range, real_rate_range) /
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range_length(rate_range);
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min_pred = given_prices[i];
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max_pred = given_prices[i];
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}
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predicted_prices.push({
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min: min_pred,
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max: max_pred,
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});
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}
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return prob;
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}
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/*
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* This corresponds to the code:
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* rate = randfloat(start_rate_min, start_rate_max);
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* for (int i = start; i < start + length; i++)
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* {
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* sellPrices[work++] = intceil(rate * basePrice);
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* rate -= randfloat(rate_decay_min, rate_decay_max);
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* }
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*
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* Would return the conditional probability given the given_prices, and modify
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* the predicted_prices array.
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* If the given_prices won't match, returns 0.
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*/
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generate_decreasing_random_price(
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given_prices, predicted_prices, start, length, start_rate_min,
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start_rate_max, rate_decay_min, rate_decay_max) {
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start_rate_min *= RATE_MULTIPLIER;
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start_rate_max *= RATE_MULTIPLIER;
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rate_decay_min *= RATE_MULTIPLIER;
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rate_decay_max *= RATE_MULTIPLIER;
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const buy_price = given_prices[0];
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let rate_pdf = new PDF(start_rate_min, start_rate_max);
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let prob = 1;
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for (let i = start; i < start + length; i++) {
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let min_pred = this.get_price(rate_pdf.min_value(), buy_price);
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let max_pred = this.get_price(rate_pdf.max_value(), buy_price);
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if (!isNaN(given_prices[i])) {
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if (given_prices[i] < min_pred - this.fudge_factor || given_prices[i] > max_pred + this.fudge_factor) {
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// Given price is out of predicted range, so this is the wrong pattern
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return 0;
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}
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// TODO: How to deal with probability when there's fudge factor?
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// Clamp the value to be in range now so the probability won't be totally biased to fudged values.
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const real_rate_range =
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this.rate_range_from_given_and_base(clamp(given_prices[i], min_pred, max_pred), buy_price);
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prob *= rate_pdf.range_limit(real_rate_range);
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if (prob == 0) {
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return 0;
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}
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min_pred = given_prices[i];
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max_pred = given_prices[i];
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}
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predicted_prices.push({
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min: min_pred,
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max: max_pred,
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});
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rate_pdf.decay(rate_decay_min, rate_decay_max);
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}
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return prob;
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}
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/*
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* This corresponds to the code:
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* rate = randfloat(rate_min, rate_max);
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* sellPrices[work++] = intceil(randfloat(rate_min, rate) * basePrice) - 1;
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* sellPrices[work++] = intceil(rate * basePrice);
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* sellPrices[work++] = intceil(randfloat(rate_min, rate) * basePrice) - 1;
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*
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* Would return the conditional probability given the given_prices, and modify
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* the predicted_prices array.
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* If the given_prices won't match, returns 0.
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*/
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generate_peak_price(
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given_prices, predicted_prices, start, rate_min, rate_max) {
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rate_min *= RATE_MULTIPLIER;
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rate_max *= RATE_MULTIPLIER;
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const buy_price = given_prices[0];
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let prob = 1;
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let rate_range = [rate_min, rate_max];
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// * Calculate the probability first.
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// Prob(middle_price)
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const middle_price = given_prices[start + 1];
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if (!isNaN(middle_price)) {
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const min_pred = this.get_price(rate_min, buy_price);
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const max_pred = this.get_price(rate_max, buy_price);
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if (middle_price < min_pred - this.fudge_factor || middle_price > max_pred + this.fudge_factor) {
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// Given price is out of predicted range, so this is the wrong pattern
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return 0;
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}
|
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// TODO: How to deal with probability when there's fudge factor?
|
||
// Clamp the value to be in range now so the probability won't be totally biased to fudged values.
|
||
const real_rate_range =
|
||
this.rate_range_from_given_and_base(clamp(middle_price, min_pred, max_pred), buy_price);
|
||
prob *= range_intersect_length(rate_range, real_rate_range) /
|
||
range_length(rate_range);
|
||
if (prob == 0) {
|
||
return 0;
|
||
}
|
||
|
||
rate_range = range_intersect(rate_range, real_rate_range);
|
||
}
|
||
|
||
const left_price = given_prices[start];
|
||
const right_price = given_prices[start + 2];
|
||
// Prob(left_price | middle_price), Prob(right_price | middle_price)
|
||
//
|
||
// A = rate_range[0], B = rate_range[1], C = rate_min, X = rate, Y = randfloat(rate_min, rate)
|
||
// rate = randfloat(A, B); sellPrices[work++] = intceil(randfloat(C, rate) * basePrice) - 1;
|
||
//
|
||
// => X->U(A,B), Y->U(C,X), Y-C->U(0,X-C), Y-C->U(0,1)*(X-C), Y-C->U(0,1)*U(A-C,B-C),
|
||
// let Z=Y-C, Z1=A-C, Z2=B-C, Z->U(0,1)*U(Z1,Z2)
|
||
// Prob(Z<=t) = integral_{x=0}^{1} [min(t/x,Z2)-min(t/x,Z1)]/ (Z2-Z1)
|
||
// let F(t, ZZ) = integral_{x=0}^{1} min(t/x, ZZ)
|
||
// 1. if ZZ < t, then min(t/x, ZZ) = ZZ -> F(t, ZZ) = ZZ
|
||
// 2. if ZZ >= t, then F(t, ZZ) = integral_{x=0}^{t/ZZ} ZZ + integral_{x=t/ZZ}^{1} t/x
|
||
// = t - t log(t/ZZ)
|
||
// Prob(Z<=t) = (F(t, Z2) - F(t, Z1)) / (Z2 - Z1)
|
||
// Prob(Y<=t) = Prob(Z>=t-C)
|
||
for (const price of [left_price, right_price]) {
|
||
if (isNaN(price)) {
|
||
continue;
|
||
}
|
||
const min_pred = this.get_price(rate_min, buy_price) - 1;
|
||
const max_pred = this.get_price(rate_range[1], buy_price) - 1;
|
||
if (price < min_pred - this.fudge_factor || price > max_pred + this.fudge_factor) {
|
||
// Given price is out of predicted range, so this is the wrong pattern
|
||
return 0;
|
||
}
|
||
// TODO: How to deal with probability when there's fudge factor?
|
||
// Clamp the value to be in range now so the probability won't be totally biased to fudged values.
|
||
const rate2_range = this.rate_range_from_given_and_base(clamp(price, min_pred, max_pred)+ 1, buy_price);
|
||
const F = (t, ZZ) => {
|
||
if (t <= 0) {
|
||
return 0;
|
||
}
|
||
return ZZ < t ? ZZ : t - t * (Math.log(t) - Math.log(ZZ));
|
||
};
|
||
const [A, B] = rate_range;
|
||
const C = rate_min;
|
||
const Z1 = A - C;
|
||
const Z2 = B - C;
|
||
const PY = (t) => (F(t - C, Z2) - F(t - C, Z1)) / (Z2 - Z1);
|
||
prob *= PY(rate2_range[1]) - PY(rate2_range[0]);
|
||
if (prob == 0) {
|
||
return 0;
|
||
}
|
||
}
|
||
|
||
// * Then generate the real predicted range.
|
||
// We're doing things in different order then how we calculate probability,
|
||
// since forward prediction is more useful here.
|
||
//
|
||
// Main spike 1
|
||
let min_pred = this.get_price(rate_min, buy_price) - 1;
|
||
let max_pred = this.get_price(rate_max, buy_price) - 1;
|
||
if (!isNaN(given_prices[start])) {
|
||
min_pred = given_prices[start];
|
||
max_pred = given_prices[start];
|
||
}
|
||
predicted_prices.push({
|
||
min: min_pred,
|
||
max: max_pred,
|
||
});
|
||
|
||
// Main spike 2
|
||
min_pred = predicted_prices[start].min;
|
||
max_pred = this.get_price(rate_max, buy_price);
|
||
if (!isNaN(given_prices[start + 1])) {
|
||
min_pred = given_prices[start + 1];
|
||
max_pred = given_prices[start + 1];
|
||
}
|
||
predicted_prices.push({
|
||
min: min_pred,
|
||
max: max_pred,
|
||
});
|
||
|
||
// Main spike 3
|
||
min_pred = this.get_price(rate_min, buy_price) - 1;
|
||
max_pred = predicted_prices[start + 1].max - 1;
|
||
if (!isNaN(given_prices[start + 2])) {
|
||
min_pred = given_prices[start + 2];
|
||
max_pred = given_prices[start + 2];
|
||
}
|
||
predicted_prices.push({
|
||
min: min_pred,
|
||
max: max_pred,
|
||
});
|
||
|
||
return prob;
|
||
}
|
||
|
||
* generate_pattern_0_with_lengths(
|
||
given_prices, high_phase_1_len, dec_phase_1_len, high_phase_2_len,
|
||
dec_phase_2_len, high_phase_3_len) {
|
||
/*
|
||
// PATTERN 0: high, decreasing, high, decreasing, high
|
||
work = 2;
|
||
// high phase 1
|
||
for (int i = 0; i < hiPhaseLen1; i++)
|
||
{
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * basePrice);
|
||
}
|
||
// decreasing phase 1
|
||
rate = randfloat(0.8, 0.6);
|
||
for (int i = 0; i < decPhaseLen1; i++)
|
||
{
|
||
sellPrices[work++] = intceil(rate * basePrice);
|
||
rate -= 0.04;
|
||
rate -= randfloat(0, 0.06);
|
||
}
|
||
// high phase 2
|
||
for (int i = 0; i < (hiPhaseLen2and3 - hiPhaseLen3); i++)
|
||
{
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * basePrice);
|
||
}
|
||
// decreasing phase 2
|
||
rate = randfloat(0.8, 0.6);
|
||
for (int i = 0; i < decPhaseLen2; i++)
|
||
{
|
||
sellPrices[work++] = intceil(rate * basePrice);
|
||
rate -= 0.04;
|
||
rate -= randfloat(0, 0.06);
|
||
}
|
||
// high phase 3
|
||
for (int i = 0; i < hiPhaseLen3; i++)
|
||
{
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * basePrice);
|
||
}
|
||
*/
|
||
|
||
const buy_price = given_prices[0];
|
||
const predicted_prices = [
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
];
|
||
let probability = 1;
|
||
|
||
// High Phase 1
|
||
probability *= this.generate_individual_random_price(
|
||
given_prices, predicted_prices, 2, high_phase_1_len, 0.9, 1.4);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
// Dec Phase 1
|
||
probability *= this.generate_decreasing_random_price(
|
||
given_prices, predicted_prices, 2 + high_phase_1_len, dec_phase_1_len,
|
||
0.6, 0.8, 0.04, 0.1);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
// High Phase 2
|
||
probability *= this.generate_individual_random_price(given_prices, predicted_prices,
|
||
2 + high_phase_1_len + dec_phase_1_len, high_phase_2_len, 0.9, 1.4);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
// Dec Phase 2
|
||
probability *= this.generate_decreasing_random_price(
|
||
given_prices, predicted_prices,
|
||
2 + high_phase_1_len + dec_phase_1_len + high_phase_2_len,
|
||
dec_phase_2_len, 0.6, 0.8, 0.04, 0.1);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
// High Phase 3
|
||
if (2 + high_phase_1_len + dec_phase_1_len + high_phase_2_len + dec_phase_2_len + high_phase_3_len != 14) {
|
||
throw new Error("Phase lengths don't add up");
|
||
}
|
||
|
||
const prev_length = 2 + high_phase_1_len + dec_phase_1_len +
|
||
high_phase_2_len + dec_phase_2_len;
|
||
probability *= this.generate_individual_random_price(
|
||
given_prices, predicted_prices, prev_length, 14 - prev_length, 0.9, 1.4);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
yield {
|
||
pattern_number: 0,
|
||
prices: predicted_prices,
|
||
probability,
|
||
};
|
||
}
|
||
|
||
* generate_pattern_0(given_prices) {
|
||
/*
|
||
decPhaseLen1 = randbool() ? 3 : 2;
|
||
decPhaseLen2 = 5 - decPhaseLen1;
|
||
hiPhaseLen1 = randint(0, 6);
|
||
hiPhaseLen2and3 = 7 - hiPhaseLen1;
|
||
hiPhaseLen3 = randint(0, hiPhaseLen2and3 - 1);
|
||
*/
|
||
for (var dec_phase_1_len = 2; dec_phase_1_len < 4; dec_phase_1_len++) {
|
||
for (var high_phase_1_len = 0; high_phase_1_len < 7; high_phase_1_len++) {
|
||
for (var high_phase_3_len = 0; high_phase_3_len < (7 - high_phase_1_len - 1 + 1); high_phase_3_len++) {
|
||
yield* this.multiply_generator_probability(
|
||
this.generate_pattern_0_with_lengths(given_prices, high_phase_1_len, dec_phase_1_len, 7 - high_phase_1_len - high_phase_3_len, 5 - dec_phase_1_len, high_phase_3_len),
|
||
1 / (4 - 2) / 7 / (7 - high_phase_1_len));
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
* generate_pattern_1_with_peak(given_prices, peak_start) {
|
||
/*
|
||
// PATTERN 1: decreasing middle, high spike, random low
|
||
peakStart = randint(3, 9);
|
||
rate = randfloat(0.9, 0.85);
|
||
for (work = 2; work < peakStart; work++)
|
||
{
|
||
sellPrices[work] = intceil(rate * basePrice);
|
||
rate -= 0.03;
|
||
rate -= randfloat(0, 0.02);
|
||
}
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * basePrice);
|
||
sellPrices[work++] = intceil(randfloat(1.4, 2.0) * basePrice);
|
||
sellPrices[work++] = intceil(randfloat(2.0, 6.0) * basePrice);
|
||
sellPrices[work++] = intceil(randfloat(1.4, 2.0) * basePrice);
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * basePrice);
|
||
for (; work < 14; work++)
|
||
{
|
||
sellPrices[work] = intceil(randfloat(0.4, 0.9) * basePrice);
|
||
}
|
||
*/
|
||
|
||
const buy_price = given_prices[0];
|
||
const predicted_prices = [
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
];
|
||
let probability = 1;
|
||
|
||
probability *= this.generate_decreasing_random_price(
|
||
given_prices, predicted_prices, 2, peak_start - 2, 0.85, 0.9, 0.03, 0.05);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
// Now each day is independent of next
|
||
let min_randoms = [0.9, 1.4, 2.0, 1.4, 0.9, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4];
|
||
let max_randoms = [1.4, 2.0, 6.0, 2.0, 1.4, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9];
|
||
for (let i = peak_start; i < 14; i++) {
|
||
probability *= this.generate_individual_random_price(
|
||
given_prices, predicted_prices, i, 1, min_randoms[i - peak_start],
|
||
max_randoms[i - peak_start]);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
}
|
||
yield {
|
||
pattern_number: 1,
|
||
prices: predicted_prices,
|
||
probability,
|
||
};
|
||
}
|
||
|
||
* generate_pattern_1(given_prices) {
|
||
for (var peak_start = 3; peak_start < 10; peak_start++) {
|
||
yield* this.multiply_generator_probability(this.generate_pattern_1_with_peak(given_prices, peak_start), 1 / (10 - 3));
|
||
}
|
||
}
|
||
|
||
* generate_pattern_2(given_prices) {
|
||
/*
|
||
// PATTERN 2: consistently decreasing
|
||
rate = 0.9;
|
||
rate -= randfloat(0, 0.05);
|
||
for (work = 2; work < 14; work++)
|
||
{
|
||
sellPrices[work] = intceil(rate * basePrice);
|
||
rate -= 0.03;
|
||
rate -= randfloat(0, 0.02);
|
||
}
|
||
break;
|
||
*/
|
||
|
||
const buy_price = given_prices[0];
|
||
const predicted_prices = [
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
];
|
||
let probability = 1;
|
||
|
||
probability *= this.generate_decreasing_random_price(
|
||
given_prices, predicted_prices, 2, 14 - 2, 0.85, 0.9, 0.03, 0.05);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
yield {
|
||
pattern_number: 2,
|
||
prices: predicted_prices,
|
||
probability,
|
||
};
|
||
}
|
||
|
||
* generate_pattern_3_with_peak(given_prices, peak_start) {
|
||
|
||
/*
|
||
// PATTERN 3: decreasing, spike, decreasing
|
||
peakStart = randint(2, 9);
|
||
// decreasing phase before the peak
|
||
rate = randfloat(0.9, 0.4);
|
||
for (work = 2; work < peakStart; work++)
|
||
{
|
||
sellPrices[work] = intceil(rate * basePrice);
|
||
rate -= 0.03;
|
||
rate -= randfloat(0, 0.02);
|
||
}
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * (float)basePrice);
|
||
sellPrices[work++] = intceil(randfloat(0.9, 1.4) * basePrice);
|
||
rate = randfloat(1.4, 2.0);
|
||
sellPrices[work++] = intceil(randfloat(1.4, rate) * basePrice) - 1;
|
||
sellPrices[work++] = intceil(rate * basePrice);
|
||
sellPrices[work++] = intceil(randfloat(1.4, rate) * basePrice) - 1;
|
||
// decreasing phase after the peak
|
||
if (work < 14)
|
||
{
|
||
rate = randfloat(0.9, 0.4);
|
||
for (; work < 14; work++)
|
||
{
|
||
sellPrices[work] = intceil(rate * basePrice);
|
||
rate -= 0.03;
|
||
rate -= randfloat(0, 0.02);
|
||
}
|
||
}
|
||
*/
|
||
|
||
const buy_price = given_prices[0];
|
||
const predicted_prices = [
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
{
|
||
min: buy_price,
|
||
max: buy_price,
|
||
},
|
||
];
|
||
let probability = 1;
|
||
|
||
probability *= this.generate_decreasing_random_price(
|
||
given_prices, predicted_prices, 2, peak_start - 2, 0.4, 0.9, 0.03, 0.05);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
// The peak
|
||
probability *= this.generate_individual_random_price(
|
||
given_prices, predicted_prices, peak_start, 2, 0.9, 1.4);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
probability *= this.generate_peak_price(
|
||
given_prices, predicted_prices, peak_start + 2, 1.4, 2.0);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
|
||
if (peak_start + 5 < 14) {
|
||
probability *= this.generate_decreasing_random_price(
|
||
given_prices, predicted_prices, peak_start + 5, 14 - (peak_start + 5),
|
||
0.4, 0.9, 0.03, 0.05);
|
||
if (probability == 0) {
|
||
return;
|
||
}
|
||
}
|
||
|
||
yield {
|
||
pattern_number: 3,
|
||
prices: predicted_prices,
|
||
probability,
|
||
};
|
||
}
|
||
|
||
* generate_pattern_3(given_prices) {
|
||
for (let peak_start = 2; peak_start < 10; peak_start++) {
|
||
yield* this.multiply_generator_probability(this.generate_pattern_3_with_peak(given_prices, peak_start), 1 / (10 - 2));
|
||
}
|
||
}
|
||
|
||
get_transition_probability(previous_pattern) {
|
||
if (typeof previous_pattern === 'undefined' || Number.isNaN(previous_pattern) || previous_pattern === null || previous_pattern < 0 || previous_pattern > 3) {
|
||
// Use the steady state probabilities of PROBABILITY_MATRIX if we don't
|
||
// know what the previous pattern was.
|
||
// See https://github.com/mikebryant/ac-nh-turnip-prices/issues/68
|
||
// and https://github.com/mikebryant/ac-nh-turnip-prices/pull/90
|
||
// for more information.
|
||
return [4530/13082, 3236/13082, 1931/13082, 3385/13082];
|
||
}
|
||
|
||
return PROBABILITY_MATRIX[previous_pattern];
|
||
}
|
||
|
||
* generate_all_patterns(sell_prices, previous_pattern) {
|
||
const generate_pattern_fns = [this.generate_pattern_0, this.generate_pattern_1, this.generate_pattern_2, this.generate_pattern_3];
|
||
const transition_probability = this.get_transition_probability(previous_pattern);
|
||
|
||
for (let i = 0; i < 4; i++) {
|
||
yield* this.multiply_generator_probability(generate_pattern_fns[i].bind(this)(sell_prices), transition_probability[i]);
|
||
}
|
||
}
|
||
|
||
* generate_possibilities(sell_prices, first_buy, previous_pattern) {
|
||
if (first_buy || isNaN(sell_prices[0])) {
|
||
for (var buy_price = 90; buy_price <= 110; buy_price++) {
|
||
const temp_sell_prices = sell_prices.slice();
|
||
temp_sell_prices[0] = temp_sell_prices[1] = buy_price;
|
||
if (first_buy) {
|
||
yield* this.generate_pattern_3(temp_sell_prices);
|
||
} else {
|
||
// All buy prices are equal probability and we're at the outmost layer,
|
||
// so don't need to multiply_generator_probability here.
|
||
yield* this.generate_all_patterns(temp_sell_prices, previous_pattern);
|
||
}
|
||
}
|
||
} else {
|
||
yield* this.generate_all_patterns(sell_prices, previous_pattern);
|
||
}
|
||
}
|
||
|
||
analyze_possibilities() {
|
||
const sell_prices = this.prices;
|
||
const first_buy = this.first_buy;
|
||
const previous_pattern = this.previous_pattern;
|
||
let generated_possibilities = [];
|
||
for (let i = 0; i < 6; i++) {
|
||
this.fudge_factor = i;
|
||
generated_possibilities = Array.from(this.generate_possibilities(sell_prices, first_buy, previous_pattern));
|
||
if (generated_possibilities.length > 0) {
|
||
console.log("Generated possibilities using fudge factor %d: ", i, generated_possibilities);
|
||
break;
|
||
}
|
||
}
|
||
|
||
const total_probability = generated_possibilities.reduce((acc, it) => acc + it.probability, 0);
|
||
for (const it of generated_possibilities) {
|
||
it.probability /= total_probability;
|
||
}
|
||
|
||
for (let poss of generated_possibilities) {
|
||
var weekMins = [];
|
||
var weekMaxes = [];
|
||
for (let day of poss.prices.slice(2)) {
|
||
// Check for a future date by checking for a range of prices
|
||
if(day.min !== day.max){
|
||
weekMins.push(day.min);
|
||
weekMaxes.push(day.max);
|
||
} else {
|
||
// If we find a set price after one or more ranged prices, the user has missed a day. Discard that data and start again.
|
||
weekMins = [];
|
||
weekMaxes = [];
|
||
}
|
||
}
|
||
if (!weekMins.length && !weekMaxes.length) {
|
||
weekMins.push(poss.prices[poss.prices.length -1].min);
|
||
weekMaxes.push(poss.prices[poss.prices.length -1].max);
|
||
}
|
||
poss.weekGuaranteedMinimum = Math.max(...weekMins);
|
||
poss.weekMax = Math.max(...weekMaxes);
|
||
}
|
||
|
||
let category_totals = {};
|
||
for (let i of [0, 1, 2, 3]) {
|
||
category_totals[i] = generated_possibilities
|
||
.filter(value => value.pattern_number == i)
|
||
.map(value => value.probability)
|
||
.reduce((previous, current) => previous + current, 0);
|
||
}
|
||
|
||
for (let pos of generated_possibilities) {
|
||
pos.category_total_probability = category_totals[pos.pattern_number];
|
||
}
|
||
|
||
generated_possibilities.sort((a, b) => {
|
||
return b.category_total_probability - a.category_total_probability || b.probability - a.probability;
|
||
});
|
||
|
||
let global_min_max = [];
|
||
for (let day = 0; day < 14; day++) {
|
||
const prices = {
|
||
min: 999,
|
||
max: 0,
|
||
};
|
||
for (let poss of generated_possibilities) {
|
||
if (poss.prices[day].min < prices.min) {
|
||
prices.min = poss.prices[day].min;
|
||
}
|
||
if (poss.prices[day].max > prices.max) {
|
||
prices.max = poss.prices[day].max;
|
||
}
|
||
}
|
||
global_min_max.push(prices);
|
||
}
|
||
|
||
generated_possibilities.unshift({
|
||
pattern_number: 4,
|
||
prices: global_min_max,
|
||
weekGuaranteedMinimum: Math.min(...generated_possibilities.map(poss => poss.weekGuaranteedMinimum)),
|
||
weekMax: Math.max(...generated_possibilities.map(poss => poss.weekMax))
|
||
});
|
||
|
||
return generated_possibilities;
|
||
}
|
||
}
|