有趣的應用 | 使用RNN預測股票價格系列一
阿新 • • 發佈：2019-01-08
import os
import pandas as pd
import pprint
import tensorflow as tf
import tensorflow.contrib.slim as slim
from data_model import StockDataSet
from model_rnn import LstmRNN flags = tf.app.flags flags.DEFINE_integer("stock_count", 100, "Stock count [100]") flags.DEFINE_integer("input_size", 5, "Input size [5]") flags.DEFINE_integer("num_steps", 30, "Num of steps [30]") flags.DEFINE_integer("num_layers", 1, "Num of layer [1]") flags.DEFINE_integer("lstm_size", 128, "Size of one LSTM cell [128]") flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]") flags.DEFINE_float("keep_prob", 0.8, "Keep probability of dropout layer. [0.8]") flags.DEFINE_float("init_learning_rate", 0.001, "Initial learning rate at early stage. [0.001]")
flags.DEFINE_float("learning_rate_decay", 0.99, "Decay rate of learning rate. [0.99]") flags.DEFINE_integer("init_epoch", 5, "Num. of epoches considered as early stage. [5]")
flags.DEFINE_integer("max_epoch", 50, "Total training epoches. [50]") flags.DEFINE_integer("embed_size", None, "If provided, use embedding vector of this size. [None]")
flags.DEFINE_string("stock_symbol", None, "Target stock symbol [None]") flags.DEFINE_integer("sample_size", 4, "Number of stocks to plot during training. [4]") flags.DEFINE_boolean("train", False, "True for training, False for testing [False]") FLAGS = flags.FLAGS pp = pprint.PrettyPrinter()
if not os.path.exists("logs"):
os.mkdir("logs")
def show_all_variables():
model_vars = tf.trainable_variables()
slim.model_analyzer.analyze_vars(model_vars, print_info=True)
def load_sp500(input_size, num_steps, k=None, target_symbol=None, test_ratio=0.05):
if target_symbol is not None:
return [
StockDataSet(
target_symbol,
input_size=input_size,
num_steps=num_steps,
test_ratio=test_ratio) ]
# Load metadata of s & p 500 stocks
info = pd.read_csv("data/constituents-financials.csv")
info = info.rename(columns={col: col.lower().replace(' ', '_') for col in info.columns}) info['file_exists'] = info['symbol'].map(lambda x: os.path.exists("data/{}.csv".format(x))) print info['file_exists'].value_counts().to_dict()
info = info[info['file_exists'] == True].reset_index(drop=True)
info = info.sort('market_cap', ascending=False).reset_index(drop=True)
if k is not None:
info = info.head(k)
print "Head of S&P 500 info:\n", info.head()
# Generate embedding meta file
info[['symbol', 'sector']].to_csv(os.path.join("logs/metadata.tsv"), sep='\t', index=False) return [ StockDataSet(row['symbol'],
input_size=input_size,
num_steps=num_steps,
test_ratio=0.05)
for _, row in info.iterrows()] def main(_): pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
import pandas as pd
import pprint
import tensorflow as tf
import tensorflow.contrib.slim as slim
from data_model import StockDataSet
from model_rnn import LstmRNN flags = tf.app.flags flags.DEFINE_integer("stock_count", 100, "Stock count [100]") flags.DEFINE_integer("input_size", 5, "Input size [5]") flags.DEFINE_integer("num_steps", 30, "Num of steps [30]") flags.DEFINE_integer("num_layers", 1, "Num of layer [1]") flags.DEFINE_integer("lstm_size", 128, "Size of one LSTM cell [128]") flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]") flags.DEFINE_float("keep_prob", 0.8, "Keep probability of dropout layer. [0.8]") flags.DEFINE_float("init_learning_rate", 0.001, "Initial learning rate at early stage. [0.001]")
flags.DEFINE_float("learning_rate_decay", 0.99, "Decay rate of learning rate. [0.99]") flags.DEFINE_integer("init_epoch", 5, "Num. of epoches considered as early stage. [5]")
flags.DEFINE_integer("max_epoch", 50, "Total training epoches. [50]") flags.DEFINE_integer("embed_size", None, "If provided, use embedding vector of this size. [None]")
flags.DEFINE_string("stock_symbol", None, "Target stock symbol [None]") flags.DEFINE_integer("sample_size", 4, "Number of stocks to plot during training. [4]") flags.DEFINE_boolean("train", False, "True for training, False for testing [False]") FLAGS = flags.FLAGS pp = pprint.PrettyPrinter()
if not os.path.exists("logs"):
os.mkdir("logs")
def show_all_variables():
model_vars = tf.trainable_variables()
slim.model_analyzer.analyze_vars(model_vars, print_info=True)
def load_sp500(input_size, num_steps, k=None, target_symbol=None, test_ratio=0.05):
if target_symbol is not None:
return [
StockDataSet(
target_symbol,
input_size=input_size,
num_steps=num_steps,
test_ratio=test_ratio) ]
# Load metadata of s & p 500 stocks
info = pd.read_csv("data/constituents-financials.csv")
info = info.rename(columns={col: col.lower().replace(' ', '_') for col in info.columns}) info['file_exists'] = info['symbol'].map(lambda x: os.path.exists("data/{}.csv".format(x))) print info['file_exists'].value_counts().to_dict()
info = info[info['file_exists'] == True].reset_index(drop=True)
info = info.sort('market_cap', ascending=False).reset_index(drop=True)
if k is not None:
info = info.head(k)
print "Head of S&P 500 info:\n", info.head()
# Generate embedding meta file
info[['symbol', 'sector']].to_csv(os.path.join("logs/metadata.tsv"), sep='\t', index=False) return [ StockDataSet(row['symbol'],
input_size=input_size,
num_steps=num_steps,
test_ratio=0.05)
for _, row in info.iterrows()] def main(_): pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(