chore: 添加Stock-Prediction-Models项目文件
添加了Stock-Prediction-Models项目的多个文件，包括数据集、模型代码、README文档和CSS样式文件。这些文件用于股票预测模型的训练和展示，涵盖了LSTM、GRU等深度学习模型的应用。
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from flask import Flask, request, jsonify
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import numpy as np
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import pickle
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import json
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from sklearn.preprocessing import MinMaxScaler
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import pandas as pd
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from datetime import datetime
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app = Flask(__name__)
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window_size = 20
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skip = 1
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layer_size = 500
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output_size = 3
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def softmax(z):
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assert len(z.shape) == 2
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s = np.max(z, axis=1)
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s = s[:, np.newaxis]
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e_x = np.exp(z - s)
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div = np.sum(e_x, axis=1)
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div = div[:, np.newaxis]
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return e_x / div
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def get_state(parameters, t, window_size = 20):
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outside = []
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d = t - window_size + 1
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for parameter in parameters:
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block = (
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parameter[d : t + 1]
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if d >= 0
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else -d * [parameter[0]] + parameter[0 : t + 1]
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)
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res = []
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for i in range(window_size - 1):
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res.append(block[i + 1] - block[i])
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for i in range(1, window_size, 1):
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res.append(block[i] - block[0])
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outside.append(res)
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return np.array(outside).reshape((1, -1))
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class Deep_Evolution_Strategy:
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inputs = None
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def __init__(
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self, weights, reward_function, population_size, sigma, learning_rate
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):
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self.weights = weights
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self.reward_function = reward_function
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self.population_size = population_size
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self.sigma = sigma
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self.learning_rate = learning_rate
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def _get_weight_from_population(self, weights, population):
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weights_population = []
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for index, i in enumerate(population):
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jittered = self.sigma * i
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weights_population.append(weights[index] + jittered)
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return weights_population
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def get_weights(self):
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return self.weights
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def train(self, epoch = 100, print_every = 1):
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lasttime = time.time()
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for i in range(epoch):
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population = []
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rewards = np.zeros(self.population_size)
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for k in range(self.population_size):
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x = []
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for w in self.weights:
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x.append(np.random.randn(*w.shape))
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population.append(x)
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for k in range(self.population_size):
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weights_population = self._get_weight_from_population(
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self.weights, population[k]
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)
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rewards[k] = self.reward_function(weights_population)
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rewards = (rewards - np.mean(rewards)) / (np.std(rewards) + 1e-7)
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for index, w in enumerate(self.weights):
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A = np.array([p[index] for p in population])
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self.weights[index] = (
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w
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+ self.learning_rate
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/ (self.population_size * self.sigma)
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* np.dot(A.T, rewards).T
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)
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if (i + 1) % print_every == 0:
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print(
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'iter %d. reward: %f'
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% (i + 1, self.reward_function(self.weights))
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)
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print('time taken to train:', time.time() - lasttime, 'seconds')
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class Model:
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def __init__(self, input_size, layer_size, output_size):
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self.weights = [
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np.random.rand(input_size, layer_size)
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* np.sqrt(1 / (input_size + layer_size)),
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np.random.rand(layer_size, output_size)
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* np.sqrt(1 / (layer_size + output_size)),
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np.zeros((1, layer_size)),
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np.zeros((1, output_size)),
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]
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def predict(self, inputs):
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feed = np.dot(inputs, self.weights[0]) + self.weights[-2]
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decision = np.dot(feed, self.weights[1]) + self.weights[-1]
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return decision
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def get_weights(self):
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return self.weights
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def set_weights(self, weights):
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self.weights = weights
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class Agent:
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POPULATION_SIZE = 15
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SIGMA = 0.1
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LEARNING_RATE = 0.03
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def __init__(self, model, timeseries, skip, initial_money, real_trend, minmax):
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self.model = model
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self.timeseries = timeseries
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self.skip = skip
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self.real_trend = real_trend
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self.initial_money = initial_money
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self.es = Deep_Evolution_Strategy(
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self.model.get_weights(),
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self.get_reward,
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self.POPULATION_SIZE,
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self.SIGMA,
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self.LEARNING_RATE,
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)
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self.minmax = minmax
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self._initiate()
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def _initiate(self):
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# i assume first index is the close value
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self.trend = self.timeseries[0]
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self._mean = np.mean(self.trend)
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self._std = np.std(self.trend)
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self._inventory = []
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self._capital = self.initial_money
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self._queue = []
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self._scaled_capital = self.minmax.transform([[self._capital, 2]])[0, 0]
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def reset_capital(self, capital):
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if capital:
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self._capital = capital
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self._scaled_capital = self.minmax.transform([[self._capital, 2]])[0, 0]
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self._queue = []
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self._inventory = []
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def trade(self, data):
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"""
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you need to make sure the data is [close, volume]
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"""
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scaled_data = self.minmax.transform([data])[0]
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real_close = data[0]
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close = scaled_data[0]
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if len(self._queue) >= window_size:
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self._queue.pop(0)
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self._queue.append(scaled_data)
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if len(self._queue) < window_size:
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return {
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'status': 'data not enough to trade',
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'action': 'fail',
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'balance': self._capital,
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'timestamp': str(datetime.now()),
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}
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state = self.get_state(
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window_size - 1,
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self._inventory,
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self._scaled_capital,
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timeseries = np.array(self._queue).T.tolist(),
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)
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action, prob = self.act_softmax(state)
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print(prob)
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if action == 1 and self._scaled_capital >= close:
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self._inventory.append(close)
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self._scaled_capital -= close
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self._capital -= real_close
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return {
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'status': 'buy 1 unit, cost %f' % (real_close),
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'action': 'buy',
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'balance': self._capital,
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'timestamp': str(datetime.now()),
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}
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elif action == 2 and len(self._inventory):
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bought_price = self._inventory.pop(0)
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self._scaled_capital += close
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self._capital += real_close
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scaled_bought_price = self.minmax.inverse_transform(
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[[bought_price, 2]]
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)[0, 0]
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try:
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invest = (
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(real_close - scaled_bought_price) / scaled_bought_price
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) * 100
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except:
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invest = 0
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return {
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'status': 'sell 1 unit, price %f' % (real_close),
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'investment': invest,
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'gain': real_close - scaled_bought_price,
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'balance': self._capital,
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'action': 'sell',
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'timestamp': str(datetime.now()),
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}
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else:
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return {
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'status': 'do nothing',
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'action': 'nothing',
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'balance': self._capital,
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'timestamp': str(datetime.now()),
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}
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def change_data(self, timeseries, skip, initial_money, real_trend, minmax):
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self.timeseries = timeseries
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self.skip = skip
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self.initial_money = initial_money
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self.real_trend = real_trend
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self.minmax = minmax
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self._initiate()
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def act(self, sequence):
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decision = self.model.predict(np.array(sequence))
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return np.argmax(decision[0])
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def act_softmax(self, sequence):
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decision = self.model.predict(np.array(sequence))
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return np.argmax(decision[0]), softmax(decision)[0]
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def get_state(self, t, inventory, capital, timeseries):
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state = get_state(timeseries, t)
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len_inventory = len(inventory)
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if len_inventory:
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mean_inventory = np.mean(inventory)
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else:
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mean_inventory = 0
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z_inventory = (mean_inventory - self._mean) / self._std
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z_capital = (capital - self._mean) / self._std
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concat_parameters = np.concatenate(
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[state, [[len_inventory, z_inventory, z_capital]]], axis = 1
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)
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return concat_parameters
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def get_reward(self, weights):
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initial_money = self._scaled_capital
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starting_money = initial_money
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invests = []
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self.model.weights = weights
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inventory = []
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state = self.get_state(0, inventory, starting_money, self.timeseries)
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for t in range(0, len(self.trend) - 1, self.skip):
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action = self.act(state)
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if action == 1 and starting_money >= self.trend[t]:
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inventory.append(self.trend[t])
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starting_money -= self.trend[t]
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elif action == 2 and len(inventory):
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bought_price = inventory.pop(0)
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starting_money += self.trend[t]
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invest = ((self.trend[t] - bought_price) / bought_price) * 100
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invests.append(invest)
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state = self.get_state(
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t + 1, inventory, starting_money, self.timeseries
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)
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invests = np.mean(invests)
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if np.isnan(invests):
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invests = 0
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score = (starting_money - initial_money) / initial_money * 100
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return invests * 0.7 + score * 0.3
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def fit(self, iterations, checkpoint):
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self.es.train(iterations, print_every = checkpoint)
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def buy(self):
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initial_money = self._scaled_capital
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starting_money = initial_money
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real_initial_money = self.initial_money
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real_starting_money = self.initial_money
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inventory = []
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real_inventory = []
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state = self.get_state(0, inventory, starting_money, self.timeseries)
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states_sell = []
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states_buy = []
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for t in range(0, len(self.trend) - 1, self.skip):
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action, prob = self.act_softmax(state)
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print(t, prob)
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if action == 1 and starting_money >= self.trend[t] and t < (len(self.trend) - 1 - window_size):
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inventory.append(self.trend[t])
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real_inventory.append(self.real_trend[t])
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real_starting_money -= self.real_trend[t]
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starting_money -= self.trend[t]
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states_buy.append(t)
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print(
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'day %d: buy 1 unit at price %f, total balance %f'
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% (t, self.real_trend[t], real_starting_money)
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)
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elif action == 2 and len(inventory):
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bought_price = inventory.pop(0)
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real_bought_price = real_inventory.pop(0)
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starting_money += self.trend[t]
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real_starting_money += self.real_trend[t]
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states_sell.append(t)
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try:
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invest = (
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(self.real_trend[t] - real_bought_price)
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/ real_bought_price
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) * 100
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except:
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invest = 0
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print(
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'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'
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% (t, self.real_trend[t], invest, real_starting_money)
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)
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state = self.get_state(
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t + 1, inventory, starting_money, self.timeseries
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)
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invest = (
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(real_starting_money - real_initial_money) / real_initial_money
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) * 100
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total_gains = real_starting_money - real_initial_money
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return states_buy, states_sell, total_gains, invest
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with open('model.pkl', 'rb') as fopen:
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model = pickle.load(fopen)
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df = pd.read_csv('TWTR.csv')
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real_trend = df['Close'].tolist()
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parameters = [df['Close'].tolist(), df['Volume'].tolist()]
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minmax = MinMaxScaler(feature_range = (100, 200)).fit(np.array(parameters).T)
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scaled_parameters = minmax.transform(np.array(parameters).T).T.tolist()
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initial_money = np.max(parameters[0]) * 2
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agent = Agent(model = model,
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timeseries = scaled_parameters,
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skip = skip,
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initial_money = initial_money,
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real_trend = real_trend,
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minmax = minmax)
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@app.route('/', methods = ['GET'])
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def hello():
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return jsonify({'status': 'OK'})
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@app.route('/inventory', methods = ['GET'])
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def inventory():
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return jsonify(agent._inventory)
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@app.route('/queue', methods = ['GET'])
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def queue():
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return jsonify(agent._queue)
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@app.route('/balance', methods = ['GET'])
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def balance():
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return jsonify(agent._capital)
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@app.route('/trade', methods = ['GET'])
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def trade():
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data = json.loads(request.args.get('data'))
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return jsonify(agent.trade(data))
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@app.route('/reset', methods = ['GET'])
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def reset():
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money = json.loads(request.args.get('money'))
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agent.reset_capital(money)
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return jsonify(True)
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if __name__ == '__main__':
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app.run(host = '0.0.0.0', port = 8005)
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