def get_trend(books, trades):
'''
Returns the linear trend in previous trades for each data point in DataFrame
of book data
'''
def trend(x):
trades_n = trades.iloc[x.trades_indexes[0]:x.trades_indexes[1]]
if len(trades_n) < 3:
return 0
else:
return linregress(trades_n.index.values, trades_n.price.values)[0]
return books.apply(trend, axis=1)
# def get_tick_df(min_ts, max_ts, live, convert_timestamps=False):
# '''
# Returns a DataFrame of ticks in time range
# '''
# if not live:
# query = {'_id': {'$gt': min_ts, '$lt': max_ts}}
# cursor = ticks_db.find(query).sort('_id', pymongo.ASCENDING)
# else:
# cursor = ticks_db.find({}).sort('$natural', pymongo.DESCENDING).limit(1)
#
# ticks = pd.DataFrame(list(cursor))
#
# if not ticks.empty:
# ticks = ticks.set_index('_id')
# if convert_timestamps:
# ticks.index = pd.to_datetime(ticks.index, unit='s')
# return ticks
#
# def get_ticks_indexes(books, ticks):
# '''
# Returns indexes of ticks closest to each data point in DataFrame
# of book data
# '''
# def ticks_indexes(ts):
# ts = int(ts)
# return ticks.index.get_loc(ts, method='nearest')
# return books.index.map(ticks_indexes)
#
# def get_buys_from_ticks(books, ticks):
# '''
# Returns a count of trades for each data point in DataFrame of book data
# '''
# def get_buy(x):
# return ticks.iloc[x.ticks_indexes].buy
# return books.apply(get_buy, axis=1)
#
# def get_sells_from_ticks(books, ticks):
# '''
# Returns a count of trades for each data point in DataFrame of book data
# '''
# def get_sell(x):
# return ticks.iloc[x.ticks_indexes].sell
# return books.apply(get_sell, axis=1)
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