algo.py

def handle_data(context, data):
    current_dt = zp.api.get_datetime()

    prices = data.history(context.stocks, "price", bar_count=200, frequency="1d")
    
    # look for new trades
    for ix, pattern in context.patterns.iterrows():
        # skip if asset is already in portfolio
        open_positions = set(context.portfolio.positions.keys())
        symbol = zp.api.symbol(pattern["symbol"])
        is_open = symbol in open_positions
        if(is_open): continue

        # check date window from handleLowDate to N days after
        is_in_window = (pattern["handleLowDate"] <= current_dt) and (pattern["handleLowDate"] >= (current_dt - pd.DateOffset(WATCHLIST_WINDOW_DAYS)))
        if (not is_in_window): continue
        
        # get symbol and price history
        price_history = prices[symbol]

        # check price above pivot
        pivot_price_date = pattern["pivotPriceDate"]
        try:
            pivot_price = price_history[pivot_price_date]
        except KeyError:
            pivot_price = None
        
        current_price = data.current(symbol, "price")
        if(current_price / pivot_price < ABOVE_PIVOT_PCT): continue

        # check short MA above long MA
        short_ma = price_history.tail(SHORT_MA_LEN).mean()
        long_ma = price_history.tail(LONG_MA_LEN).mean()
        if(long_ma > short_ma): continue

        # add new position and update previous ones
        open_positions.add(symbol)
        target_pct = 1 / len(open_positions)
        for position in open_positions:
            zp.api.order_target_percent(position, target_pct)
        context.position_dates[symbol] = current_dt
    
    # look for closing positions
    open_positions = context.portfolio.positions
    for position in open_positions.values():
        current_price = position.last_sale_price
        buy_price = position.cost_basis
        should_take_profit = (current_price / buy_price) > TAKE_PROFIT_PCT
        should_stop_loss = (current_price / buy_price) < STOP_LOSS_PCT
        does_exceed_patience = (current_dt - pd.DateOffset(PATIENCE_WINDOW_DAYS)) >= context.position_dates[position.asset]
        should_close_position = should_take_profit or does_exceed_patience or should_stop_loss
        if(should_close_position): zp.api.order_target_percent(position.asset, 0)