从Akshare库获取上证指数数据代码
时间: 2023-09-26 20:06:58 浏览: 163
以下是从Akshare库获取上证指数数据的Python代码:
```python
import akshare as ak
# 获取上证指数数据
sz_index_df = ak.stock_zh_index_daily(symbol="sh000001")
print(sz_index_df)
```
需要注意的是,您需要先安装akshare库,可以使用以下命令进行安装:
```python
!pip install akshare
```
相关问题
使用akshare获取股票数据强化学习完整代码
以下是使用akshare获取股票数据并进行强化学习的完整代码示例:
```python
import akshare as ak
import pandas as pd
import numpy as np
import random
from collections import deque
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
from tensorflow.keras.optimizers import Adam
# 获取股票数据
stock_zh_a_daily = ak.stock_zh_a_daily(symbol='sh600000', adjust="hfq")
df = pd.DataFrame(stock_zh_a_daily)
df = df[['open', 'high', 'low', 'close', 'volume']]
df['H-L'] = df['high'] - df['low']
df['O-C'] = df['open'] - df['close']
df = df[['close', 'H-L', 'O-C', 'volume']]
df = df[-1000:] # 取最近1000天的数据
print(df.head())
# 定义强化学习环境
class Environment:
def __init__(self, data, initial_investment=20000):
self.actions = ["buy", "sell", "hold"]
self.data = data
self.n_step = len(self.data) - 1
self.initial_investment = initial_investment
self.current_step = None
self.stock_owned = None
self.cash_in_hand = None
self.action_history = []
self.state_history = []
self.reward_history = []
self.value_history = []
def reset(self):
self.current_step = 0
self.stock_owned = 0
self.cash_in_hand = self.initial_investment
self.action_history = []
self.state_history = []
self.reward_history = []
self.value_history = []
return self._get_observation()
def step(self, action):
assert action in self.actions
# 更新当前持仓
prev_val = self._get_val()
self.action_history.append(action)
self.current_step += 1
self.state_history.append(self._get_observation())
if action == "sell":
self.stock_owned -= 1
self.cash_in_hand += self.data.iloc[self.current_step]['close']
self.reward_history.append(self.data.iloc[self.current_step]['close'] - prev_val)
elif action == "buy":
self.stock_owned += 1
self.cash_in_hand -= self.data.iloc[self.current_step]['close']
self.reward_history.append(prev_val - self.data.iloc[self.current_step]['close'])
else:
self.reward_history.append(0)
self.value_history.append(self._get_val())
done = (self.current_step == self.n_step)
info = {"stock_owned": self.stock_owned, "cash_in_hand": self.cash_in_hand}
return self._get_observation(), sum(self.reward_history), done, info
def _get_observation(self):
obs = np.array([self.data.iloc[self.current_step]['close'], self.data.iloc[self.current_step]['H-L'],
self.data.iloc[self.current_step]['O-C'], self.data.iloc[self.current_step]['volume']])
return obs
def _get_val(self):
return self.stock_owned * self.data.iloc[self.current_step]['close'] + self.cash_in_hand
# 构建神经网络模型
class ReplayBuffer:
def __init__(self, obs_dim, act_dim, size=int(1e6)):
self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)
self.act_buf = np.zeros([size, act_dim], dtype=np.float32)
self.rew_buf = np.zeros(size, dtype=np.float32)
self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)
self.done_buf = np.zeros(size, dtype=np.float32)
self.ptr, self.size, self.max_size = 0, 0, size
def store(self, obs, act, rew, next_obs, done):
self.obs_buf[self.ptr] = obs
self.act_buf[self.ptr] = act
self.rew_buf[self.ptr] = rew
self.next_obs_buf[self.ptr] = next_obs
self.done_buf[self.ptr] = done
self.ptr = (self.ptr+1) % self.max_size
self.size = min(self.size+1, self.max_size)
def sample_batch(self, batch_size=32):
idxs = np.random.randint(0, self.size, size=batch_size)
return dict(obs=self.obs_buf[idxs],
act=self.act_buf[idxs],
rew=self.rew_buf[idxs],
next_obs=self.next_obs_buf[idxs],
done=self.done_buf[idxs])
def get_model(obs_dim, act_dim):
model = Sequential([
Dense(256, input_shape=(obs_dim,), activation='relu'),
Dense(256, activation='relu'),
Dense(256, activation='relu'),
Dense(act_dim)
])
return model
# 训练模型
def train():
env = Environment(df)
obs_dim = env.reset().shape[0]
act_dim = len(env.actions)
model = get_model(obs_dim, act_dim)
target_model = get_model(obs_dim, act_dim)
target_model.set_weights(model.get_weights())
replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim)
def get_action(state, epsilon):
if random.random() < epsilon:
return random.choice(env.actions)
else:
state = np.expand_dims(state, axis=0)
q_values = model.predict(state)
return env.actions[np.argmax(q_values)]
def compute_loss(batch):
obs, act, rew, next_obs, done = batch['obs'], batch['act'], batch['rew'], batch['next_obs'], batch['done']
target_q = target_model.predict(next_obs).max(axis=1)
target_q = rew + (1-done) * gamma * target_q
q = model.predict(obs)
q = tf.reduce_sum(q * tf.one_hot(act, act_dim), axis=1)
loss = tf.reduce_mean((q - target_q)**2)
return loss
optimizer = Adam(learning_rate=lr)
epsilon = 1.0
gamma = 0.99
batch_size = 32
updates_per_step = 10
update_target_every = 2000
replay_start_size = 10000
total_timesteps = 200000
start_steps = 10000
step_count = 0
episode_reward = 0
episode_timesteps = 0
state = env.reset()
for i in range(total_timesteps):
if i < start_steps:
action = random.choice(env.actions)
else:
action = get_action(state, epsilon)
next_state, reward, done, info = env.step(action)
replay_buffer.store(state, env.actions.index(action), reward, next_state, done)
state = next_state
episode_reward += reward
episode_timesteps += 1
step_count += 1
if len(replay_buffer.obs_buf) < replay_start_size:
continue
if step_count % updates_per_step == 0:
for j in range(updates_per_step):
batch = replay_buffer.sample_batch(batch_size=batch_size)
loss = compute_loss(batch)
grads = tf.gradients(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if step_count % update_target_every == 0:
target_model.set_weights(model.get_weights())
if done or (episode_timesteps == env.n_step):
print('Episode: {}, episode reward: {}, episode timesteps: {}'.format(i, episode_reward, episode_timesteps))
state = env.reset()
episode_reward, episode_timesteps = 0, 0
train()
```
这段代码中,我们首先使用akshare获取上证指数的股票数据,并对数据进行处理。然后我们定义了一个强化学习环境类`Environment`,可以根据当前的股票数据状态,选择买入、卖出或持有不动。接下来我们用神经网络模型来拟合这个环境,并使用经验回放缓存`ReplayBuffer`来加速训练。最后,我们使用`train()`函数来训练模型,并输出训练过程中的奖励和步数信息。
用akshare获取数据
好的,关于使用 akshare 获取数据的方法,我可以给你一些简单的介绍。akshare 是一个基于 Python 的开源金融数据接口库,它可以获取各种金融数据,例如股票数据、期货数据、基金数据、汇率数据等。
首先,你需要在 Python 环境下安装 akshare 库,安装方法如下:
```
pip install akshare
```
安装完成后,你可以使用 akshare 库提供的各种函数来获取不同的金融数据。例如,如果你想获取股票历史数据,可以使用如下代码:
```python
import akshare as ak
# 获取上证指数历史行情数据
stock_zh_index_daily_df = ak.stock_zh_index_daily(symbol="sh000001")
# 打印数据
print(stock_zh_index_daily_df)
```
这段代码会获取上证指数的历史行情数据,并将其存储在 pandas 的 DataFrame 中。你可以根据自己的需要对数据进行处理和分析。
当然,akshare 库提供的函数不止这些,你可以查看官方文档了解更多的接口和使用方法。
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