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Low Capital Robinhood Penny Stock Trading Algo - LOW BETA

Been messing around with some algos, I have a variation of this paper trading that's not doing as good as this.

I've built an integration bridge with RH and Quantopian now that they cut brokerages. Ill test this on my bridge next week and see if its stable

Clone Algorithm
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Backtest from to with initial capital
Total Returns
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Alpha
--
Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
from quantopian.pipeline import Pipeline
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume
from quantopian.pipeline.filters.morningstar import IsPrimaryShare

import numpy as np #needed for NaN handling
import math #ceil and floor are useful for rounding

from itertools import cycle

def initialize(context):
    #set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.50))
    set_slippage(slippage.VolumeShareSlippage(volume_limit=.20, price_impact=0.0))
    #set_slippage(slippage.FixedSlippage(spread=0.00))
    set_commission(commission.PerTrade(cost=0.00))
    #set_slippage(slippage.FixedSlippage(spread=0.00))
    set_long_only()

    context.MaxCandidates=30
    context.MaxBuyOrdersAtOnce=10
    context.MyLeastPrice=0.40
    context.MyMostPrice=1.40
    context.MyFireSalePrice=context.MyLeastPrice
    context.MyFireSaleAge=3

    # over simplistic tracking of position age
    context.age={}
    print len(context.portfolio.positions)
    # Rebalance
    EveryThisManyMinutes=105
    TradingDayHours=6.5
    TradingDayMinutes=int(TradingDayHours*60)
    for minutez in xrange(
        1, 
        TradingDayMinutes, 
        EveryThisManyMinutes
    ):
        schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(minutes=minutez))

    # Prevent excessive logging of canceled orders at market close.
    schedule_function(cancel_open_orders, date_rules.every_day(), time_rules.market_close(hours=0, minutes=1))

    # Record variables at the end of each day.
    schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())

    # Create our pipeline and attach it to our algorithm.
    my_pipe = make_pipeline(context)
    attach_pipeline(my_pipe, 'my_pipeline')

def make_pipeline(context):
    """
    Create our pipeline.
    """

    # Filter for primary share equities. IsPrimaryShare is a built-in filter.
    primary_share = IsPrimaryShare()

    # Equities listed as common stock (as opposed to, say, preferred stock).
    # 'ST00000001' indicates common stock.
    common_stock = morningstar.share_class_reference.security_type.latest.eq('ST00000001')

    # Non-depositary receipts. Recall that the ~ operator inverts filters,
    # turning Trues into Falses and vice versa
    not_depositary = ~morningstar.share_class_reference.is_depositary_receipt.latest

    # Equities not trading over-the-counter.
    not_otc = ~morningstar.share_class_reference.exchange_id.latest.startswith('OTC')

    # Not when-issued equities.
    not_wi = ~morningstar.share_class_reference.symbol.latest.endswith('.WI')

    # Equities without LP in their name, .matches does a match using a regular
    # expression
    not_lp_name = ~morningstar.company_reference.standard_name.latest.matches('.* L[. ]?P.?$')

    # Equities with a null value in the limited_partnership Morningstar
    # fundamental field.
    not_lp_balance_sheet = morningstar.balance_sheet.limited_partnership.latest.isnull()

    # Equities whose most recent Morningstar market cap is not null have
    # fundamental data and therefore are not ETFs.
    have_market_cap = morningstar.valuation.market_cap.latest.notnull()

    # At least a certain price
    price = USEquityPricing.close.latest
    AtLeastPrice   = (price >= context.MyLeastPrice)
    AtMostPrice    = (price <= context.MyMostPrice)

    # Filter for stocks that pass all of our previous filters.
    tradeable_stocks = (
        primary_share
        & common_stock
        & not_depositary
        & not_otc
        & not_wi
        & not_lp_name
        & not_lp_balance_sheet
        & have_market_cap
        & AtLeastPrice
        & AtMostPrice
    )

    LowVar=6
    HighVar=40

    log.info('\nAlgorithm initialized variables:\n context.MaxCandidates %s \n LowVar %s \n HighVar %s'
        % (context.MaxCandidates, LowVar, HighVar)
    )

    # High dollar volume filter.
    base_universe = AverageDollarVolume(
        window_length=20,
        mask=tradeable_stocks
    ).percentile_between(LowVar, HighVar)

    # Short close price average.
    ShortAvg = SimpleMovingAverage(
        inputs=[USEquityPricing.close],
        window_length=3,
        mask=base_universe
    )

    # Long close price average.
    LongAvg = SimpleMovingAverage(
        inputs=[USEquityPricing.close],
        window_length=45,
        mask=base_universe
    )

    percent_difference = (ShortAvg - LongAvg) / LongAvg

    # Filter to select securities to long.
    stocks_worst = percent_difference.bottom(context.MaxCandidates)
    securities_to_trade = (stocks_worst)

    return Pipeline(
        columns={
            'stocks_worst': stocks_worst
        },
        screen=(securities_to_trade),
    )

def my_compute_weights(context):
    """
    Compute ordering weights.
    """
    # Compute even target weights for our long positions and short positions.
    stocks_worst_weight = 1.00/len(context.stocks_worst)

    return stocks_worst_weight

def before_trading_start(context, data):
    # Gets our pipeline output every day.
    context.output = pipeline_output('my_pipeline')

    context.stocks_worst = context.output[context.output['stocks_worst']].index.tolist()

    print context.stocks_worst
    context.stocks_worst_weight = my_compute_weights(context)

    context.MyCandidate = cycle(context.stocks_worst)
    
    context.LowestPrice=context.MyLeastPrice #reset beginning of day
    print len(context.portfolio.positions)
    for stock in context.portfolio.positions:
        CurrPrice = float(data.current([stock], 'price'))
        if CurrPrice<context.LowestPrice:
            context.LowestPrice = CurrPrice
        if stock in context.age:
            context.age[stock] += 1
        else:
            context.age[stock] = 1
    for stock in context.age:
        if stock not in context.portfolio.positions:
            context.age[stock] = 0
        message = 'stock.symbol: {symbol}  :  age: {age}'
        log.info(message.format(symbol=stock.symbol, age=context.age[stock]))

    pass

def my_rebalance(context, data):
    BuyFactor=.99
    SellFactor=1.01
    cash=context.portfolio.cash

    cancel_open_buy_orders(context, data)

    # Order sell at profit target in hope that somebody actually buys it
    for stock in context.portfolio.positions:
        if not get_open_orders(stock):
            StockShares = context.portfolio.positions[stock].amount
            CurrPrice = float(data.current([stock], 'price'))
            CostBasis = float(context.portfolio.positions[stock].cost_basis)
            SellPrice = float(make_div_by_05(CostBasis*SellFactor, buy=False))
            if np.isnan(SellPrice):
                pass # probably best to wait until nan goes away
            elif (
                stock in context.age 
                and context.MyFireSaleAge<=context.age[stock] 
                and (
                    context.MyFireSalePrice>CurrPrice
                    or CostBasis>CurrPrice
                )
            ):
                SellPrice = float(make_div_by_05(.95*CurrPrice, buy=False))
                order(stock, -StockShares,
                    style=LimitOrder(SellPrice)
                )
            else:
                order(stock, -StockShares,
                    style=LimitOrder(SellPrice)
                )

    WeightThisBuyOrder=float(1.00/context.MaxBuyOrdersAtOnce)
    for ThisBuyOrder in range(context.MaxBuyOrdersAtOnce):
        stock = context.MyCandidate.next()
        PH = data.history([stock], 'price', 20, '1d')
        PH_Avg = float(PH.mean())
        CurrPrice = float(data.current([stock], 'price'))
        if np.isnan(CurrPrice):
            pass # probably best to wait until nan goes away
        else:
            if CurrPrice > float(1.25*PH_Avg):
                BuyPrice=float(CurrPrice)
            else:
                BuyPrice=float(CurrPrice*BuyFactor)
            BuyPrice=float(make_div_by_05(BuyPrice, buy=True))
            StockShares = int(WeightThisBuyOrder*cash/BuyPrice)
            order(stock, StockShares,
                style=LimitOrder(BuyPrice)
            )

#if cents not divisible by .05, round down if buy, round up if sell
def make_div_by_05(s, buy=False):
    s *= 20.00
    s =  math.floor(s) if buy else math.ceil(s)
    s /= 20.00
    return s

def my_record_vars(context, data):
    """
    Record variables at the end of each day.
    """

    # Record our variables.
    record(leverage=context.account.leverage)
    record(positions=len(context.portfolio.positions))
    if 0<len(context.age):
        MaxAge=context.age[max(context.age.keys(), key=(lambda k: context.age[k]))]
        print MaxAge
        record(MaxAge=MaxAge)
    record(LowestPrice=context.LowestPrice)

def log_open_order(StockToLog):
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        if stock == StockToLog:
            for order in orders:
                message = 'Found open order for {amount} shares in {stock}'
                log.info(message.format(amount=order.amount, stock=stock))

def log_open_orders():
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        for order in orders:
            message = 'Found open order for {amount} shares in {stock}'
            log.info(message.format(amount=order.amount, stock=stock))

def cancel_open_buy_orders(context, data):
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        for order in orders:
            #message = 'Canceling order of {amount} shares in {stock}'
            #log.info(message.format(amount=order.amount, stock=stock))
            if 0<order.amount: #it is a buy order
                cancel_order(order)

def cancel_open_orders(context, data):
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        for order in orders:
            #message = 'Canceling order of {amount} shares in {stock}'
            #log.info(message.format(amount=order.amount, stock=stock))
            cancel_order(order)

# This is the every minute stuff
def handle_data(context, data):
    pass
There was a runtime error.
7 responses

Bridge? You are still able to link with Robinhood?

I have developed a very rudimentary connection between the two.

Do you code any languages? (JavaScript?)

I’m working on a far more stable one which should be done this weekend. 15 min delays are still there but I don’t day trade anyways. So I won’t sell within the day.

I only glanced at the code real briefly (I'm on my phone), but even with $1,000, you are being way too optimistic using 0.0% price slippage.

I’ll adjust it tonight

Is there a reason this does not work when you give it large amounts of money to start with?

Maybe because they're penny stocks? and orders that big aren't going to fill? I had the same problem. Hilariously small returns when backtesting >1M. I haven't looked at the code yet though.

How's the bridge you were working on? I've been thinking about dumping my robinhood account and going to IB, but I sure am going to miss the zero commission.

Who test in live ?