simple future mm

simplest_mm.py

import bisect

def calc_pnl_futs(actions,observations):
    pnls=[]
    if observations.last_valid_index() == None:
        return np.zeros(len(observations))
    settle = np.mean(observations[observations.last_valid_index()])
    for a,o in zip(actions,observations):
        if a<0:
            pnls.append(o[0]-settle)
        elif a>0:
            pnls.append(settle-o[1])
        else:
            pnls.append(0)
    return np.array(pnls) * 5

def market_make_futs(observed,predicted,initial_edge,inventory_martingales=[1,1.5,2,2.5]):
    actions = []
    inventory = 0
    for o,p in zip(observed,predicted):
        edge_to_add = (bisect.bisect(inventory_martingales,abs(inventory))+1) * initial_edge
        edge_to_unwind = (bisect.bisect(inventory_martingales,abs(inventory))-1) * -1 * initial_edge
        print inventory,edge_to_add,edge_to_unwind
        if inventory<inventory_martingales[-1] and ((p-o[1])>edge_to_add or (inventory<0 and (p-o[1])>edge_to_unwind)):
            actions.append(1)
           # print 'Buying...Inventory: ',inventory, ' Edge: ', (p-o[1]), o, p
            inventory+=1
        elif inventory>(-1*inventory_martingales[-1]) and ((o[0]-p)>edge_to_add or (inventory>0 and (o[0]-p)>edge_to_unwind)):
            actions.append(-1)
            #print 'Selling...Inventory: ',inventory, ' Edge: ', (o[0]-p), o, p
            inventory-=1
        else:
            actions.append(0)
    actions = np.array(actions)
    return [actions,calc_pnl_futs(actions,observed)]


b = interesting_data.fut_bid.dropna()
a = interesting_data.fut_ask.dropna()
m = (b+a)/2
s = pd.Series(zip(b,a),index=b.index)
m_kf = pd.Series(univariate_kf(m.values,m[m.first_valid_index()],1,1e6),index=m.index)
actions,pnls = market_make_futs(s[::100],m_kf[::100],4)