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913 lines
30 KiB
JavaScript
913 lines
30 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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* 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 - 0.5), (x + 0.5)) 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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*/
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constructor(a, b, uniform = true) {
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this.value_start = Math.round(a);
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this.value_end = Math.round(b);
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const range = [a, b];
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const total_length = range_length(range);
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this.prob = Array(this.value_end - this.value_start + 1);
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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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range_of(idx) {
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// TODO: consider doing the "exclusive end" properly.
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return [this.value_start + idx - 0.5, this.value_start + idx + 0.5 - 1e-9];
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}
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min_value() {
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return this.value_start - 0.5;
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}
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max_value() {
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return this.value_end + 0.5 - 1e-9;
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}
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normalize() {
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const total_probability = this.prob.reduce((acc, it) => acc + it, 0);
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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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}
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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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let prob = 0;
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const start_idx = Math.round(start) - this.value_start;
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const end_idx = Math.round(end) - this.value_start;
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for (let i = start_idx; i <= end_idx; i++) {
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const bucket_prob = this.prob[i] * range_intersect_length(this.range_of(i), range);
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this.prob[i] = bucket_prob;
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prob += bucket_prob;
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}
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this.prob = this.prob.slice(start_idx, end_idx + 1);
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this.value_start = Math.round(start);
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this.value_end = Math.round(end);
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this.normalize();
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return prob;
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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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*/
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decay(rate_decay_min, rate_decay_max) {
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const ret = new PDF(
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this.min_value() - rate_decay_max, this.max_value() - rate_decay_min, false);
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/*
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// O(n^2) naive algorithm for reference, which would be too slow.
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for (let i = this.value_start; i <= this.value_end; i++) {
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const unit_prob = this.prob[i - this.value_start] / (rate_decay_max - rate_decay_min) / 2;
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for (let j = rate_decay_min; j < rate_decay_max; j++) {
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// ([i - 0.5, i + 0.5] uniform) - ([j, j + 1] uniform)
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// -> [i - j - 1.5, i + 0.5 - j] with a triangular PDF
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// -> approximate by
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// [i - j - 1.5, i - j - 0.5] uniform &
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// [i - j - 0.5, i - j + 0.5] uniform
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ret.prob[i - j - 1 - ret.value_start] += unit_prob; // Part A
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ret.prob[i - j - ret.value_start] += unit_prob; // Part B
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}
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}
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*/
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// Transform to "CDF"
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for (let i = 1; i < this.prob.length; i++) {
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this.prob[i] += this.prob[i - 1];
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}
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// Return this.prob[l - this.value_start] + ... + this.prob[r - 1 - this.value_start];
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// This assume that this.prob is already transformed to "CDF".
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const sum = (l, r) => {
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l -= this.value_start;
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r -= this.value_start;
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if (l < 0) l = 0;
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if (r > this.prob.length) r = this.prob.length;
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if (l >= r) return 0;
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return this.prob[r - 1] - (l == 0 ? 0 : this.prob[l - 1]);
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};
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for (let x = 0; x < ret.prob.length; x++) {
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// i - j - 1 - ret.value_start == x (Part A)
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// -> i = x + j + 1 + ret.value_start, j in [rate_decay_min, rate_decay_max)
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ret.prob[x] = sum(x + rate_decay_min + 1 + ret.value_start, x + rate_decay_max + 1 + ret.value_start);
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// i - j - ret.value_start == x (Part B)
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// -> i = x + j + ret.value_start, j in [rate_decay_min, rate_decay_max)
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ret.prob[x] += sum(x + rate_decay_min + ret.value_start, x + rate_decay_max + ret.value_start);
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}
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this.prob = ret.prob;
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this.value_start = ret.value_start;
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this.value_end = ret.value_end;
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this.normalize();
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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?
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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(middle_price, 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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if (prob == 0) {
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return 0;
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}
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rate_range = range_intersect(rate_range, real_rate_range);
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}
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const left_price = given_prices[start];
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const right_price = given_prices[start + 2];
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// Prob(left_price | middle_price), Prob(right_price | middle_price)
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//
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// A = rate_range[0], B = rate_range[1], C = rate_min, X = rate, Y = randfloat(rate_min, rate)
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// rate = randfloat(A, B); sellPrices[work++] = intceil(randfloat(C, rate) * basePrice) - 1;
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//
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// => 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),
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// let Z=Y-C, Z1=A-C, Z2=B-C, Z->U(0,1)*U(Z1,Z2)
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// Prob(Z<=t) = integral_{x=0}^{1} [min(t/x,Z2)-min(t/x,Z1)]/ (Z2-Z1)
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// let F(t, ZZ) = integral_{x=0}^{1} min(t/x, ZZ)
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// 1. if ZZ < t, then min(t/x, ZZ) = ZZ -> F(t, ZZ) = ZZ
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// 2. if ZZ >= t, then F(t, ZZ) = integral_{x=0}^{t/ZZ} ZZ + integral_{x=t/ZZ}^{1} t/x
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// = t - t log(t/ZZ)
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// Prob(Z<=t) = (F(t, Z2) - F(t, Z1)) / (Z2 - Z1)
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// Prob(Y<=t) = Prob(Z>=t-C)
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for (const price of [left_price, right_price]) {
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if (isNaN(price)) {
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continue;
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}
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const min_pred = this.get_price(rate_min, buy_price) - 1;
|
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const max_pred = this.get_price(rate_range[1], buy_price) - 1;
|
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if (price < min_pred - this.fudge_factor || 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?
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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 rate2_range = this.rate_range_from_given_and_base(clamp(price, min_pred, max_pred)+ 1, buy_price);
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const F = (t, ZZ) => {
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if (t <= 0) {
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return 0;
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}
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return ZZ < t ? ZZ : t - t * (Math.log(t) - Math.log(ZZ));
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};
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const [A, B] = rate_range;
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const C = rate_min;
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const Z1 = A - C;
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const Z2 = B - C;
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const PY = (t) => (F(t - C, Z2) - F(t - C, Z1)) / (Z2 - Z1);
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prob *= PY(rate2_range[1]) - PY(rate2_range[0]);
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if (prob == 0) {
|
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return 0;
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}
|
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}
|
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|
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// * Then generate the real predicted range.
|
|
// We're doing things in different order then how we calculate probability,
|
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// since forward prediction is more useful here.
|
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//
|
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// Main spike 1
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let min_pred = this.get_price(rate_min, buy_price) - 1;
|
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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_description: i18next.t("patterns.fluctuating"),
|
|
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_description: i18next.t("patterns.large-spike"),
|
|
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_description: i18next.t("patterns.decreasing"),
|
|
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_description: i18next.t("patterns.small-spike"),
|
|
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_description: i18next.t("patterns.all"),
|
|
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;
|
|
}
|
|
}
|