本文整理汇总了Python中timeseries.TimeSeries.get_closing_price_list方法的典型用法代码示例。如果您正苦于以下问题：Python TimeSeries.get_closing_price_list方法的具体用法？Python TimeSeries.get_closing_price_list怎么用？Python TimeSeries.get_closing_price_list使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类timeseries.TimeSeries的用法示例。
在下文中一共展示了TimeSeries.get_closing_price_list方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from timeseries import TimeSeries [as 别名]
# 或者: from timeseries.TimeSeries import get_closing_price_list [as 别名]
class Stock:
""""""
LONG_TERM_TIMESPAN = 10
SHORT_TERM_TIMESPAN = 5
#----------------------------------------------------------------------
def __init__(self, symbol):
"""Constructor"""
self.symbol = symbol
self.history = TimeSeries()
self.updated = Event()
#----------------------------------------------------------------------
@property
def price(self):
""""""
try:
return self.history[-1].value
except IndexError:
return None
#----------------------------------------------------------------------
def update(self, timestamp, price):
if price < 0:
raise ValueError("price should not be negative")
self.history.update(timestamp, price)
self.updated.fire(self)
#----------------------------------------------------------------------
def is_increasing_trend(self):
""""""
return self.history[-3].value < self.history[-2].value < \
self.history[-1].value
#----------------------------------------------------------------------
def _is_crossover_below_to_above(self, prev_ma, prev_reference_ma,
current_ma, current_reference_ma):
return prev_ma < prev_reference_ma \
and current_ma > current_reference_ma
#----------------------------------------------------------------------
def get_crossover_signal(self, on_date):
""""""
NUM_DAYS = self.LONG_TERM_TIMESPAN + 1
closing_price_list = self.history.get_closing_price_list(on_date, NUM_DAYS)
# Return NEUTRAL signal
if len(closing_price_list) < 11:
return 0
long_term_series = closing_price_list[-self.LONG_TERM_TIMESPAN:]
prev_long_term_series = closing_price_list[-self.LONG_TERM_TIMESPAN-1:-1]
short_term_series = closing_price_list[-self.SHORT_TERM_TIMESPAN:]
prev_short_term_series = closing_price_list[-self.SHORT_TERM_TIMESPAN-1:-1]
long_term_ma = sum([update.value for update in long_term_series]) \
/self.LONG_TERM_TIMESPAN
prev_long_term_ma = sum([update.value for update in prev_long_term_series])\
/self.LONG_TERM_TIMESPAN
short_term_ma = sum([update.value for update in short_term_series])\
/self.SHORT_TERM_TIMESPAN
prev_short_term_ma = sum([update.value for update in prev_short_term_series])\
/self.SHORT_TERM_TIMESPAN
# BUY signal
if self._is_crossover_below_to_above(prev_short_term_ma,
prev_long_term_ma,
short_term_ma,
long_term_ma):
return StockSignal.buy
# BUY signal
if self._is_crossover_below_to_above(prev_long_term_ma,
prev_short_term_ma,
long_term_ma,
short_term_ma):
return StockSignal.sell
# NEUTRAL signal
return StockSignal.neutral