def formatData(lines):
stock = Stock()
if len(lines) > 1:
values = lines[0].split(' ')
stock.id = values[0]
i = 1
while values[i] != '日线':
stock.name += values[i]
i += 1
lines.pop(0)
if len(lines) <= 1:
return stock
for line in lines:
dayvalue = DayValue()
values = line.split('\t')
if getTime(values[0]) < time.strptime('2017/1/01', '%Y/%m/%d'):
continue
dayvalue.date = values[0]
dayvalue.open = float(values[1])
dayvalue.high = float(values[2])
dayvalue.low = float(values[3])
dayvalue.close = float(values[4])
dayvalue.tradeamount = float(values[5])
dayvalue.tradevolume = float(values[6])
stock.dayvalues.append(dayvalue)
return stock
def getTradeInfoAfter(date, stockname, days) -> [RangeTrend]:
stock = Stock.fromJson(loadStock(stockname))
if stock is None:
return []
index = 0
for dayvalue in stock.dayvalues:
if getTime(dayvalue.date) > date:
break
index += 1
index -= 1
if index >= len(stock.dayvalues):
return []
results = []
for i in days:
if index + i < len(stock.dayvalues):
trend = getRangeTrend(index, i, stock.dayvalues)
results.append(trend)
return results
def getCloseAfter(date, stockname, days) -> [int]:
stock = Stock.fromJson(loadStock(stockname))
if stock is None:
return []
index = 0
for dayvalue in stock.dayvalues:
if getTime(dayvalue.date) > date:
break
index += 1
index -= 1
results = []
for i in days:
if index + i < len(stock.dayvalues):
results.append(100 * (stock.dayvalues[index + i].close -
stock.dayvalues[index].close) /
stock.dayvalues[index].close)
else:
results.append(0)
return results
def addStock(self, config):
stock = Stock(config["id"], config["name"], config["area"],
config["ex"])
print(" Stock: " + stock.getFullID() + " " + stock.getName())
self.stockSet[stock.getFullID()] = stock
stock.setInfo(config)
pass
def loadFromDB():
stocks = []
client = MongoClient('localhost', 27017)
db = client["test"]
coll = db['stocks']
results = coll.find({}, {'_id': 0})
for r in results:
stocks.append(Stock.fromJson(r))
return stocks
def loadAllStockFromDB() -> Dict[str, Stock]:
stocks = dict()
items = DatabaseMgr.instance().stocks.find({}, {'_id': 0})
for item in items:
if 'id' in item:
print(item['id'])
stockId = item['id']
stock = Stock.fromJson(item)
stock.calcMinsAndMaxs()
stocks[stockId] = stock
return stocks
from data.stock import Stock
from data.storemgr import StockMgr
items = DatabaseMgr.instance().stocks.find({'id': '002807'}, {'_id': 0})
stock = None
for item in items:
if 'id' in item:
# print(item['id'])
stockId = item['id']
stock = Stock.fromJson(item)
break
value = stock.findHeighestValue('2017/1/1')
value0 = stock.findLowestValue(value.date)
value1 = stock.findHeighestValue(value0.date)
if value1 is not None:
if (value1.high - value0.low) / value0.open < 0.15:
pass
import sys
from data.stock import Stock
from predictor import Predictor
from score import auc
from preprocess import Preprocess
submission_number = sys.argv[1]
print submission_number
def submission(ids, preds):
name = "submissions/submission{0}.csv".format(submission_number)
f = open(name, 'w')
if len(ids) != len(preds):
raise Exception("The number of IDs and the number of predictions are different")
string = 'id,prediction\n'
for index,i in enumerate(ids):
string += str(i) + ',' + str(preds[index]) + "\n"
f.write(str(string))
f.close()
data, targets, _ = Stock.train()
holdout_data, ids = Stock.test()
assert len(data) == len(targets)
Predictor.train(data, targets)
preds = Predictor.multi_predict(holdout_data)
print preds[0:50]
submission(ids, preds)
from data.stock import Stock from predictor import Predictor from score import auc from preprocess import Preprocess data, targets, cv_targets = Stock.train() #data = Preprocess.scale(data) #cv_targets = Preprocess.scale(cv_targets) #data = Preprocess.polynomial(data, 5) half = len(data)/2 tr_data, holdout_data = data[:half], data[half:] train_targets, holdout_targets = targets[:half], targets[half:] Predictor.train(tr_data, train_targets) preds = Predictor.multi_predict(holdout_data) print 'AUC ', auc(preds, cv_targets) print Predictor.multi_predict([[0.]]) print Predictor.multi_predict([[0.1]]) print Predictor.multi_predict([[-0.1]]) print Predictor.multi_predict([[0.01]]) print Predictor.multi_predict([[-0.01]])
def test():
print("Test StockProxySina")
stock1 = Stock('01810', '小米集团-W', 'hangkong', 'hk')
stock2 = Stock('002415', '海康威视', 'shenzhen', 'sz')
sinaproxy = StockProxySina()
stock1.addProxy(sinaproxy)
print(stock1.getFullID() + "\tprice:" + str(stock1.getPrice()) + "\t" +
str(stock1.getPriceDatetime()))
stock2.addProxy(sinaproxy)
print(stock2.getFullID() + "\tprice:" + str(stock2.getPrice()) + "\t" +
str(stock2.getPriceDatetime()))
from data.stock import Stock
data, targets = Stock.train(categorize=False)
pos, neg = [], []
for delta in targets:
if delta > 0:
pos.append(delta)
elif delta < 0:
neg.append(delta)
print sum(pos)/len(pos)
print sum(neg)/len(neg)
