# 基于Random Forest的决策策略 > 来源：https://uqer.io/community/share/54a10ef8f9f06c4bb886324b 版本：1.0 作者：李丞 联系：cheng.li@datayes.com 利用随机树分类算法，通过历史价格的上升状态变化规律，预测下一日股价变动的方向。预测上涨则买入，下跌则卖出（如果可以的话）； ```py from sklearn.ensemble import RandomForestClassifier from collections import deque import pandas as pd import numpy as np start = pd.datetime(2010, 4, 1) end = pd.datetime(2014, 9, 16) longest_history = 1 bm = 'HS300' universe = ['600000.XSHG'] csvs = [] capital_base = 1e5 refresh_rate = 1 window_length = 10 def initialize(account): account.security = universe[0] account.window_length = window_length account.classifier = RandomForestClassifier() # 先进先出的deque序列，设定了最长的长度，在序列超过最长长度的时候，会将头部序列移出 account.recent_prices = deque(maxlen=account.window_length+2) # 保存最近的股价 account.X = deque(maxlen=100) # 自变量 account.Y = deque(maxlen=100) # 应变量 account.prediction = 0 # 保存最近的预测值 def handle_data(account): hist = account.get_history(1) if account.security in hist: account.recent_prices.append(hist[account.security]['closePrice'][0]) # 更新最近的股价 if len(account.recent_prices) >= account.window_length+2: # 如果我们已经获取了足够的股价 RecentPrice=list(account.recent_prices) # 将deque转换为对应的list # 制作一组1和0，标记股价是否相对于上一日价格上升。 changes = np.diff(RecentPrice) > 0 account.X.append(RecentPrice[1:-1]) account.Y.append(changes[-1]) if len(account.Y) >= 100: # 已经拥有足够的数据im account.classifier.fit(account.X, account.Y) # 设定模型 account.prediction = account.classifier.predict(changes[1:]) # 预测 # 如果过大0.5，买入；小于0.5，卖出 if account.prediction > 0.5: buyAmount = int(account.position.cash / hist[account.security]['closePrice'][0]) order(account.security, buyAmount) else: order_to(account.security, 0) ```