Intelligently Distributed Capital

Quantitative Strategies for Intelligent Capital Allocation to the ecosystem of blockchain and other Distributed Ledger technologies

def initialize(context):
context.secs = [sid(8554),sid(22972),sid(25485),sid(26669),sid(26807)]
leverage = 1.0
context.top_k = 1
context.weight = leverage/context.top_k
import numpy as np
@batch_transform(refresh_period=20, window_length=61)
def trailing_return(datapanel):
if datapanel['price'] is None: return None
pricedf = np.log(datapanel['price'])
return pricedf.ix[-1]-pricedf.ix[0]
def reweight(context,data,wt,min_pct_diff=0.1):
liquidity =
orders = {}
pct_diff = 0
for sec in wt.keys():
target = liquidity*wt[sec]/data[sec].price
current = context.portfolio.positions[sec].amount
orders[sec] = target-current
pct_diff += abs(orders[sec]*data[sec].price/liquidity)
if pct_diff > min_pct_diff:"%s ordering %d" % (sec, target-current)))
for sec in orders.keys(): order(sec, orders[sec])
def handle_data(context, data):
ranks = trailing_return(data)
abs_mom = lambda x: data[x].mavg(20)-data[x].mavg(200)
if ranks is None: return
ranked_secs = sorted(context.secs, key=lambda x: ranks[x], reverse=True)
top_secs = ranked_secs[0:context.top_k]
wt = dict(((sec,context.weight if sec in top_secs and abs_mom(sec) > 0 else 0.0) for sec in context.secs))