def __init__(self):
Strategy.__init__(self)
from marketsim._pub import strategy, side
self._seller = strategy.price.Ladder(self.orderFactory, self.initialSize, side.Sell())
self._buyer = strategy.price.Ladder(self.orderFactory, self.initialSize, side.Buy())
event.subscribe(self._seller.on_order_created, _(self)._send, self)
event.subscribe(self._buyer.on_order_created, _(self)._send, self)
def clearPosition(self):
from marketsim._pub import order, side
position = self._position()
pending = self._pendingVolume()
if position is not None and pending is not None:
self._internalSuspended = True
self.suspended = True
# signed number of assets that we have
p = position + pending
if p > 0: # if positive, we sell
self._send(order.Market(side.Sell(), constant(p))())
if p < 0: # if negative, we buy
self._send(order.Market(side.Buy(), constant(-p))())
def __call__(self):
return side.Buy() if self._impl() > 0 else side.Sell()
def orderBooksToRender(ctx, traders):
books = list(set(itertools.chain(*[t.orderBooks for t in traders])))
books = filter(lambda b: type(b) is Local_StringFloatIntListITimeSerie,
books)
graphs = ctx.graphs
def orderbook_ts():
from marketsim.gen._out.math._max import Max
thisBook = orderbook.Proxy()
assetPrice = thisBook.MidPrice
scaled = (assetPrice - 100) / 10
def bollinger(avg, stddev):
return [
assetPrice, avg, (avg + stddev * 2).OnEveryDt(1),
(avg - stddev * 2).OnEveryDt(1)
]
ts = {
ctx.price_graph:
[assetPrice, thisBook.Asks.BestPrice, thisBook.Bids.BestPrice],
ctx.askbid_graph: [
thisBook.Asks.LastTradePrice,
thisBook.Asks.WeightedPrice(), thisBook.Bids.LastTradePrice,
thisBook.Bids.WeightedPrice()
],
ctx.avgs_graph: [
assetPrice.Cumulative.Avg,
(assetPrice.Moving(20).Avg, config.collectMoving),
(assetPrice.Moving(100).Avg, config.collectMoving),
assetPrice.EW(0.15).Avg,
assetPrice.EW(0.65).Avg,
assetPrice.EW(0.015).Avg,
],
ctx.macd_graph: [
scaled,
scaled.EW(2. / 27).Avg,
scaled.EW(2. / 13).Avg,
assetPrice.macd().Value,
assetPrice.macd().Signal(),
assetPrice.macd().Histogram(),
((assetPrice.LogReturns() * 100).OnEveryDt(1),
config.collectMoving)
],
ctx.minmax_graph: [
assetPrice,
assetPrice.Cumulative.MaxEpsilon(),
assetPrice.Cumulative.MinEpsilon(),
(assetPrice.Moving(100.).Maximum, config.collectMoving),
(assetPrice.Moving(100.).Minimum, config.collectMoving)
],
ctx.bollinger_100_graph:
bollinger(
assetPrice.Moving(100).Avg,
assetPrice.Moving(100).StdDev) if config.collectMoving else [],
ctx.bollinger_20_graph:
bollinger(assetPrice.Moving(20).Avg,
assetPrice.Moving(20).StdDev)
if config.collectMoving else [],
ctx.bollinger_a015_graph:
bollinger(assetPrice.EW(0.015).Avg,
assetPrice.EW(0.015).StdDev),
}
out = []
for (graph, timeserie_list) in ts.iteritems():
for timeserie in timeserie_list:
if type(timeserie) is tuple:
if timeserie[1]:
timeserie = timeserie[0]
else:
continue
out.append(makeTimeSerie(timeserie, graph))
return out
for b in books:
thisBook = orderbook.Proxy()
ts = orderbook_ts()
b.volumes_graph = ctx.addGraph("Volume levels " + b.label)
ts.append(
volumeLevels(
thisBook.Queue(side.Sell()).VolumeLevels(30, 10),
b.volumes_graph))
ts.append(
volumeLevels(
thisBook.Queue(side.Buy()).VolumeLevels(30, 10),
b.volumes_graph))
b.timeseries = ts
if config.collectRSI:
b.rsi_graph = ctx.addGraph("RSI " + b.label)
ts.append(TimeSerie(thisBook.MidPrice, b.rsi_graph))
for timeframe in [ #0.,
#0.001,
#0.01,
0.1,
#0.3,
0.5,
1.,
#1.5,
2,
#3,
#4,
5
]:
ts.append(
TimeSerie(
thisBook.MidPrice.RSI(timeframe,
1. / 14).Value.OnEveryDt(1),
b.rsi_graph))
return books