Stock Screening

[backtrader]

Use the link below to go the original post

Click here to see the full blog post

[cwse]

How do you stop the output printing:
"""- Datas:
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
- Data0:"""
for each data source added?

[backtrader]

You cannot with the standard WriterFile which is the one creating the output.

You can if you subclass it, check the sources, and create your own output.

[cwse]

Thank you. How do I subclass it, check the sources and create a different output?

[backtrader]

There is no other way.

In any case the expected (even if small) API of the Writer should have a small documentation page to ease up subclassing.

[spyamine]

Thank you for this example. can you please complete it by using this screener as input to a strategy. for instance to be long under and short the long list and close the trades if they go out of the list. thx a lot

[backtrader]

See this reddit post: Reddit - The Conservative Formula in Python: Quantitative Investing made Easy

The code

class St(bt.Strategy):
    params = dict(
        rperiod=1,  # period for the returns calculation, default 1 period
        vperiod=36,  # lookback period for volatility - default 36 periods
        mperiod=12,  # lookback period for momentum - default 12 periods
        torank=100,  # number of stocks to rank as top
        reserve=0.05  # 5% reserve capital
    )

    def __init__(self):
        # Return, volatility and momentum
        rets = [bt.ind.PctChange(d, period=self.p.rperiod) for d in self.datas]
        vols = [bt.ind.StdDev(ret, period=self.p.vperiod) for ret in rets]
        moms = [bt.ind.ROC(d, period=self.p.mperiod) for d in self.datas]

        # simple rank formula: (momentum * net payout) / volatility
        # The highest ranked: low vol, large momentum, large payout
        self.ranks = [d.npy * m / v for d, v, m in zip(self.datas, vols, moms)]

        # allocation perc pro stock
        # reserve kept to make sure orders are not rejected due to
        # margin. Prices are calculated when known (close), but orders can only
        # be executed next day (opening price). Price can gap upwards
        self.perctarget = (100.0 - self.p.reserve) % self.p.torank

    def next(self):
        # get the ranks sorted for each iteration in a list
        ranks = sorted(
            ((self.datas[i], rank[0]) for i, rank in enumerate(self.ranks)),
            key=lambda x: x[1],  # use rank (elem 1 in the tuple) to sort
            reverse=True,  # highest ranked 1st ... please
        )
        # Put top 100 in ordereddict with data as key to test for presence
        r100 = collections.OrderedDict(ranks[:self.p.torank])

        # Remove those no longer Top 100, prepare quick lookup dict
        positions = {pos: pos.data for pos in self.getpositions() if pos}
        for pos, data in positions.items():
            if data not in r100:  # open and no rank100 ... close
                self.order_target_percent(data, target=0.0)

        # Add the newcomers to the Top 100, prepare easy reverse lookup
        revpositions = {data: pos for pos, data in positions.items()}
        for data in (d for d in r100 if d not in revpositions):
            self.order_target_percent(data, target=self.perctarget)

[spyamine]

@backtrader You are awesome!! many thanks

[spyamine]

@backtrader Hi, I've tryed to test your code but unfortunatly it didn't work properly as the compiler do not reconignize "pos.data" from this line "{pos: pos.data for pos in self.getpositions() if pos}" and also i suspect this line too "self.order_target_percent(data, target=self.perctarget)" the self.order_target percent part. Can you please share the full code that works for you?

[spyamine]

from backtrader.feeds import PandasData

class PycaishenData(PandasData):

# Add a 'pe' line to the inherited ones from the base class
lines = ('turnover','mkt_cap')

# openinterest in GenericCSVData has index 7 ... add 1
# add the parameter to the parameters inherited from the base class
params = (('turnover', -1),('mkt_cap',-1))

class StAmine(bt.Strategy):
params = dict(
rperiod=1, # period for the returns calculation, default 1 period
vperiod=36, # lookback period for volatility - default 36 periods
mperiod=12, # lookback period for momentum - default 12 periods
torank=10, # number of stocks to rank as top
reserve=0.05 # 5% reserve capital
)

def __init__(self):


    # The highest ranked: mkt_cap

    self.ranks = [bt.ind.movav(d.mkt_cap,period = 1) for d in self.datas]

    # allocation perc pro stock
    # reserve kept to make sure orders are not rejected due to
    # margin. Prices are calculated when known (close), but orders can only
    # be executed next day (opening price). Price can gap upwards
    self.perctarget = (100.0 - self.p.reserve) % self.p.torank

def next(self):
    # get the ranks sorted for each iteration in a list
    ranks = sorted(
        ((self.datas[i], rank[0]) for i, rank in enumerate(self.ranks)),
        key=lambda x: x[1],  # use rank (elem 1 in the tuple) to sort
        reverse=True,  # highest ranked 1st ... please
    )
    # Put top 100 in ordereddict with data as key to test for presence
    r100 = collections.OrderedDict(ranks[:self.p.torank])

    # Remove those no longer Top 100, prepare quick lookup dict
    positions = {pos: pos.data for pos in self.getpositions().values() if pos}
    for pos, data in positions.items():
        if data not in r100:  # open and no rank100 ... close
            self.order_target_percent(data, target=0.0)

    # Add the newcomers to the Top 100, prepare easy reverse lookup
    revpositions = {data: pos for pos, data in positions.items()}
    for data in (d for d in r100 if d not in revpositions):
        self.order_target_percent(data, target=self.perctarget)

def run(tickers, datas,strategy = StAmine,plotdetails= True,writedetail = False):

cerebro = bt.Cerebro()

print ('Starting Portfolio Value: %.2f ' % cerebro.broker.getvalue())

cerebro.addstrategy(strategy)

for ticker, data in zip(tickers , datas):
    dt = PycaishenData(dataname=data)
    if plotdetails == False:
        dt.plotinfo.plot = False
    cerebro.adddata(dt,ticker)

cerebro.broker.set_fundmode(True)
cerebro.broker.set_fundstartval(10.0)  # the default is 100
cerebro.broker.setcommission(commission=0.0025)

# Add the Analyzers
cerebro.addanalyzer(btanalyser.SQN)
cerebro.addanalyzer(btanalyser.AnnualReturn)
cerebro.addanalyzer(btanalyser.SharpeRatio)

cerebro.broker.setcash(100000.0)

cerebro.addanalyzer(btanalyser.TradeAnalyzer)

if writedetail:
    cerebro.addwriter(bt.WriterFile, csv=True, rounding=4)

cerebro.run()

cerebro.plot()

print ('Final portfolio value: %.2f' % cerebro.broker.getvalue())

run(tickers, datas,strategy = StAmine,plotdetails= False,writedetail = False)

the code seems to do nothing!!

[backtrader]

@spyamine said in Stock Screening:

Hi, I've tryed to test your code but unfortunatly it didn't work properly as the compiler do not reconignize "pos.data" from this line "{pos: pos.data for pos in self.getpositions() if pos}" and also i suspect this line too "self.order_target_percent(data, target=self.perctarget)" the self.order_target percent part. Can you please share the full code that works for you?

It is a hand-crafted snippet, giving an indication and orientation as to how a proposed algorithm could be implemented.