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import pymongo
import pandas as pd
from math import log
from time import time
import sys
from scipy.stats import linregress
import pickle
client = pymongo.MongoClient()
db = client['bitmicro']
def get_book_df(symbol, limit, convert_timestamps=False):
Returns a DataFrame of book data
books_db = db[symbol+'_books']
cursor = books_db.find().sort('_id', -1).limit(limit)
books = pd.DataFrame(list(cursor))
books = books.set_index('_id')
if convert_timestamps:
books.index = pd.to_datetime(books.index, unit='s')
def to_df(x):
return pd.DataFrame(x[:10])
return books.applymap(to_df).sort_index()
def get_width_and_mid(books):
Returns width of best market and midpoint for each data point in DataFrame
of book data
best_bid = books.bids.apply(lambda x: x.price[0])
best_ask = books.asks.apply(lambda x: x.price[0])
return best_ask-best_bid, (best_bid + best_ask)/2
def get_future_mid(books, offset, sensitivity):
Returns percent change of future midpoints for each data point in DataFrame
of book data
def future(timestamp):
i = books.index.get_loc(timestamp+offset, method='nearest')
if abs(books.index[i] - (timestamp+offset)) < sensitivity:
return books.mid.iloc[i]
return (
def get_power_imbalance(books, n=10, power=2):
Returns a measure of the imbalance between bids and offers for each data
point in DataFrame of book data
def calc_imbalance(book):
def calc(x):
return x.amount*(.5*book.width/(x.price-book.mid))**power
bid_imbalance = book.bids.iloc[:n].apply(calc, axis=1)
ask_imbalance = book.asks.iloc[:n].apply(calc, axis=1)
return (bid_imbalance-ask_imbalance).sum()
imbalance = books.apply(calc_imbalance, axis=1)
return imbalance
def get_power_adjusted_price(books, n=10, power=2):
Returns the percent change of an average of order prices weighted by inverse
distance-wieghted volume for each data point in DataFrame of book data
def calc_adjusted_price(book):
def calc(x):
return x.amount*(.5*book.width/(x.price-book.mid))**power
bid_inv = 1/book.bids.iloc[:n].apply(calc, axis=1)
ask_inv = 1/book.asks.iloc[:n].apply(calc, axis=1)
bid_price = book.bids.price.iloc[:n]
ask_price = book.asks.price.iloc[:n]
return (bid_price*bid_inv + ask_price*ask_inv).sum() /\
(bid_inv + ask_inv).sum()
adjusted = books.apply(calc_adjusted_price, axis=1)
return (adjusted/books.mid).apply(log).fillna(0)
def get_trade_df(symbol, min_ts, max_ts, convert_timestamps=False):
Returns a DataFrame of trades for symbol in time range
trades_db = db[symbol+'_trades']
query = {'timestamp': {'$gt': min_ts, '$lt': max_ts}}
cursor = trades_db.find(query).sort('_id', pymongo.ASCENDING)
trades = pd.DataFrame(list(cursor))
if not trades.empty:
trades = trades.set_index('_id')
if convert_timestamps:
trades.index = pd.to_datetime(trades.index, unit='s')
return trades
def get_trades_indexes(books, trades, offset, live=False):
Returns indexes of trades in offset range for each data point in DataFrame
of book data
def indexes(ts):
ts = int(ts)
i_0 = trades.timestamp.searchsorted([ts-offset], side='left')[0]
if live:
i_n = -1
i_n = trades.timestamp.searchsorted([ts-1], side='right')[0]
return (i_0, i_n)
def get_trades_count(books, trades):
Returns a count of trades for each data point in DataFrame of book data
def count(x):
return len(trades.iloc[x.indexes[0]:x.indexes[1]])
return books.apply(count, axis=1)
def get_trades_average(books, trades):
Returns the percent change of a volume-weighted average of trades for each
data point in DataFrame of book data
def mean_trades(x):
trades_n = trades.iloc[x.indexes[0]:x.indexes[1]]
if not trades_n.empty:
return (trades_n.price*trades_n.amount).sum()/trades_n.amount.sum()
return (books.mid/books.apply(mean_trades, axis=1)).apply(log).fillna(0)
def get_aggressor(books, trades):
Returns a measure of whether trade aggressors were buyers or sellers for
each data point in DataFrame of book data
def aggressor(x):
trades_n = trades.iloc[x.indexes[0]:x.indexes[1]]
if trades_n.empty:
return 0
buys = trades_n['type'] == 'buy'
buy_vol = trades_n[buys].amount.sum()
sell_vol = trades_n[~buys].amount.sum()
return buy_vol - sell_vol
return books.apply(aggressor, axis=1)
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.indexes[0]:x.indexes[1]]
if len(trades_n) < 3:
return 0
return linregress(trades_n.index.values, trades_n.price.values)[0]
return books.apply(trend, axis=1)
def check_times(books):
Returns list of differences between collection time and max book timestamps
for verification purposes
time_diff = []
for i in range(len(books)):
book = books.iloc[i]
ask_ts = max(book.asks.timestamp)
bid_ts = max(book.bids.timestamp)
ts = max(ask_ts, bid_ts)
return time_diff
def make_features(symbol, sample, mid_offsets,
trades_offsets, powers, live=False):
Returns a DataFrame with targets and features
start = time()
stage = time()
# Book related features:
books = get_book_df(symbol, sample)
if not live:
print 'get book data run time:', (time()-stage)/60, 'minutes'
stage = time()
books['width'], books['mid'] = get_width_and_mid(books)
if not live:
print 'width and mid run time:', (time()-stage)/60, 'minutes'
stage = time()
for n in mid_offsets:
books['mid{}'.format(n)] = get_future_mid(books, n, sensitivity=1)
if not live:
books = books.dropna()
print 'offset mids run time:', (time()-stage)/60, 'minutes'
stage = time()
for p in powers:
books['imbalance{}'.format(p)] = get_power_imbalance(books, 10, p)
books['adj_price{}'.format(p)] = get_power_adjusted_price(books, 10, p)
if not live:
print 'power calcs run time:', (time()-stage)/60, 'minutes'
stage = time()
books = books.drop(['bids', 'asks'], axis=1)
# Trade related features:
min_ts = books.index.min() - trades_offsets[-1]
max_ts = books.index.max()
if live:
max_ts += 10
trades = get_trade_df(symbol, min_ts, max_ts)
for n in trades_offsets:
if trades.empty:
books['indexes'] = 0
books['t{}_count'.format(n)] = 0
books['t{}_av'.format(n)] = 0
books['agg{}'.format(n)] = 0
books['trend{}'.format(n)] = 0
books['indexes'] = get_trades_indexes(books, trades, n, live)
books['t{}_count'.format(n)] = get_trades_count(books, trades)
books['t{}_av'.format(n)] = get_trades_average(books, trades)
books['agg{}'.format(n)] = get_aggressor(books, trades)
books['trend{}'.format(n)] = get_trend(books, trades)
if not live:
print 'trade features run time:', (time()-stage)/60, 'minutes'
stage = time()
print 'make_features run time:', (time()-start)/60, 'minutes'
return books.drop('indexes', axis=1)
def make_data(symbol, sample):
Convenience function for calling make_features
data = make_features(symbol,
trades_offsets=[30, 60, 120, 180],
powers=[2, 4, 8])
return data
if __name__ == '__main__' and len(sys.argv) == 4:
data = make_data(sys.argv[1], int(sys.argv[2]))
with open(sys.argv[3], 'w+') as f:
pickle.dump(data, f)