Python api.record函数代码示例

def handle_data(context, data):
    # Skip first 300 days to get full windows
    context.i += 1
    dp = context.history_container.digest_panels
    for k in dp.keys():
        df = dp[k].buffer['price']
        a = df.dropna()
        print('No.',context.i,':Len.',len(a))
        print('Contents:')        
        print(a)
        
    print(context.history_container.buffer_panel.buffer['price'])

    if context.i < 40:
        return

    # Compute averages
    # history() has to be called with the same params
    # from above and returns a pandas dataframe.
    short_mavg = history(20, '1d', 'price').mean()
    long_mavg = history(40, '1d', 'price').mean()

    # Trading logic
    if short_mavg[context.sym] > long_mavg[context.sym]:
        # order_target orders as many shares as needed to
        # achieve the desired number of shares.
        order_target(context.sym, 100)
    elif short_mavg[context.sym] < long_mavg[context.sym]:
        order_target(context.sym, 0)

    # Save values for later inspection
    record(AAPL=data[context.sym].price,
           short_mavg=short_mavg[context.sym],
           long_mavg=long_mavg[context.sym])

def handle_data(context, data):
    short_ema = context.short_ema_trans.handle_data(data)
    long_ema = context.long_ema_trans.handle_data(data)
    if short_ema is None or long_ema is None:
        return

    buy = False
    sell = False

    if (short_ema > long_ema).all() and not context.invested:
        order(context.asset, 100)
        context.invested = True
        buy = True
    elif (short_ema < long_ema).all() and context.invested:
        order(context.asset, -100)
        context.invested = False
        sell = True

    record(
        AAPL=data[context.asset].price,
        short_ema=short_ema[context.asset],
        long_ema=long_ema[context.asset],
        buy=buy,
        sell=sell,
    )

def handle_data(context, data):
    # Skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:
        return

    # Compute averages
    # history() has to be called with the same params
    # from above and returns a pandas dataframe.
    short_mavg = history(100, '1d', 'price').mean()
    long_mavg = history(300, '1d', 'price').mean()
    # price_history = data.history(assets=symbol('TEST'), fields="price", bar_count=5, frequency="1d")

    # Trading logic
    if short_mavg[0] > long_mavg[0]:
        # order_target orders as many shares as needed to
        # achieve the desired number of shares.
        order_target(symbol('AAPL'), 100)
    elif short_mavg[0] < long_mavg[0]:
        order_target(symbol('AAPL'), 0)

    # Save values for later inspection
    record(AAPL=data[symbol('AAPL')].price,
           short_mavg=short_mavg[0],
           long_mavg=long_mavg[0])

def rebalance(context, data):

    # Pipeline data will be a dataframe with boolean columns named 'longs' and
    # 'shorts'.
    pipeline_data = context.pipeline_data
    all_assets = pipeline_data.index

    longs = all_assets[pipeline_data.longs]
    shorts = all_assets[pipeline_data.shorts]

    record(universe_size=len(all_assets))

    # Build a 2x-leveraged, equal-weight, long-short portfolio.
    one_third = 1.0 / 3.0
    for asset in longs:
        order_target_percent(asset, one_third)

    for asset in shorts:
        order_target_percent(asset, -one_third)

    # Remove any assets that should no longer be in our portfolio.
    portfolio_assets = longs | shorts
    positions = context.portfolio.positions
    for asset in viewkeys(positions) - set(portfolio_assets):
        # This will fail if the asset was removed from our portfolio because it
        # was delisted.
        if data.can_trade(asset):
            order_target_percent(asset, 0)

def handle_data_macd(context, data):
    context.i += 1
    if context.i < 60:
        return

    buy = False
    sell = False

    sym = symbol(code)

    count = int(100000 /  data[sym].price)

    prices = history(40, '1d', 'price')
    macd = prices.apply(MACD, fastperiod=12, slowperiod=26, signalperiod=9)
 
    if context.investment == False:
        if macd[sym] > 0 and context.position == -1:
            order_target(sym, count)
            context.investment = True
            context.buy_price = data[sym].price
            buy = True
            context.position = 1
    else:
        if (data[sym].price > context.buy_price + (context.buy_price * sell_point)):
            order_target(sym, -count)
            context.investment = False
            sell = True

    if macd[sym] < 0 :
        context.position = -1
    
    if macd[sym] > 0 :
        context.position = 1
            
    record(code=data[sym].price, macd=macd[sym], buy=buy, sell=sell)

def handle_data(context, data):
    print context.portfolio.portfolio_value
    # Skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:
        return

    # Compute averages
    # history() has to be called with the same params
    # from above and returns a pandas dataframe.
    short_mavg = history(100, '1d', 'price').mean()
    long_mavg = history(300, '1d', 'price').mean()

    sym = symbol('AAPL')

    # Trading logic
    if short_mavg[sym] > long_mavg[sym]:
        # order_target orders as many shares as needed to
        # achieve the desired number of shares.
        order_target(sym, 100)
    elif short_mavg[sym] < long_mavg[sym]:
        order_target(sym, 0)

    # Save values for later inspection
    record(AAPL=data[sym].price,
           short_mavg=short_mavg[sym],
           long_mavg=long_mavg[sym])

def handle_data(context, data):
    
    #trading algorithm (executed on every event)
    
    #skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:
        return
    
    #compute short and long moving averages:
    short_mavg = history(100, '1d', 'price').mean()
    long_mavg = history(300, '1d', 'price').mean()
    
    buy = False
    sell = False
    
    #trading logic
    if (short_mavg[0] > long_mavg[0]) and not context.invested:
        buy = True
        context.invested = True
        order_target(symbol('AAPL'), 100)        
    elif (short_mavg[0] < long_mavg[0]) and context.invested:
        sell = True
        context.invested = False
        order_target(symbol('AAPL'), -100)
    
    #save values for plotting
    record(AAPL = data[symbol('AAPL')].price,
           short_mavg = short_mavg[0],
           long_mavg = long_mavg[0],
           buy=buy,
           sell=sell)

def handle_data(context, data):
    #assert context.portfolio.cash > 0.0, "ERROR: negative context.portfolio.cash"
    #assert len(context.raw_data) == context.training_data_length; "ERROR: "
    
    # data stored as (open, high, low, close, volume, price)
    feed_data = ([  
                    data[context.security].open, 
                    data[context.security].high,
                    data[context.security].low,
                    data[context.security].close
                    #data[context.security].volume,
                    #data[context.security].close,
    ])
    
    #keep track of history. 
    context.raw_data.pop(0)
    context.raw_data.append(feed_data)
    context.normalized_data = Manager.preprocessData(context.raw_data)[:-2]
    prediction = context.strategy.predict(context.normalized_data)[-1]
    print "Value: $%.2f    Cash: $%.2f    Predict: %.5f" % (context.portfolio.portfolio_value, context.portfolio.cash, prediction[0])

    # Do nothing if there are open orders:
    if has_orders(context, data):
        print('has open orders - doing nothing!')
    # Put entire position in
    elif prediction > 0.5:
        order_target_percent(context.security, .95)
    # Take entire position out
    else:
        order_target_percent(context.security, 0)
        #order_target_percent(context.security, -.99)
    record(BENCH=data[context.security].price)
    record(SPY=data[context.benchmark].price)

def handle_data_bband(context, data):
    context.i += 1
    if context.i < 20:
        return

    buy = False
    sell = False

    sym = symbol(code)

    count = int(100000 /  data[sym].price)

    prices = history(20, '1d', 'price')
    upper, middle, lower = ta.BBANDS(
        prices[sym].values,
        timeperiod=20,
        nbdevup=2,
        nbdevdn=2,
        matype=0)
 
    if context.investment == False:
        if lower[-1] > data[sym].price:
            order_target(sym, count)
            context.investment = True
            context.buy_price = data[sym].price
            buy = True
            context.position = 1
    else:
        if (data[sym].price > context.buy_price + (context.buy_price * sell_point)):
            order_target(sym, -count)
            context.investment = False
            sell = True
            
    record(code=data[sym].price, upper=upper[-1], lower=lower[-1], makeBacktestingDataFrame=middle[-1], buy=buy, sell=sell)

def handle_data_magc(context, data):
    context.i += 1
    if context.i < 60:
        return

    ma20 = history(20, '1d', 'price').mean()
    ma60 = history(60, '1d', 'price').mean()

    buy = False
    sell = False

    sym = symbol(code)

    count = int(100000 /  data[sym].price)
    
    if context.investment == False:
        if ma20[sym] > ma60[sym] :
            order_target(sym, count)
            context.investment = True
            context.buy_price = data[sym].price
            buy = True
    else:
        if (data[sym].price > context.buy_price + (context.buy_price * sell_point)):
            order_target(sym, -count)
            context.investment = False
            sell = True
            
    record(code=data[sym].price, ma20=ma20[sym], ma60=ma60[sym], buy=buy, sell=sell)

def handle_data(context, data):
    print "================================="
    print "New iteration"
    print data

    order(symbol('AAPL'), 10)

    record(AAPL=data[symbol('AAPL')].price)

def handle_data(context, data):
    context.i += 1
    stock_name = context.panel.axes[0][0]
    if context.i == 60:
        order(symbol(stock_name), 10)
    if context.i == 150:
        order(symbol(stock_name), -10)
    record(Prices=data[symbol(stock_name)].price)

def handle_data(context, data):
    if context.prev_day_close_price:
        percent_change = (data[0]["close"] - context.prev_day_close_price) * 100 / context.prev_day_close_price
        open_close_percent_change = (data[0]["open"] - context.prev_day_close_price) * 100 / context.prev_day_close_price

        if percent_change < 0:
            context.pattern.append(-1)
        else:
            context.pattern.append(1)

        pattern_length = len(context.buy_pattern_to_match)
        if context.pattern[-pattern_length:] == context.buy_pattern_to_match:

            order(context.symbol, 10, limit_price = data[0]["open"])

            if context.take_profit_target and (data[0]["open"] + context.take_profit_target) <= data[0]["high"]:
                target_price = data[0]["open"] + context.take_profit_target
                order(context.symbol, -10, limit_price = target_price)
                pnl_cents = target_price - data[0]["open"]
                context.cents = context.cents + pnl_cents
                print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "BUY", pnl_cents, context.cents)
            else:
                order(context.symbol, -10, limit_price = data[0]["close"])
                pnl_cents = data[0]["close"] - data[0]["open"]
                context.cents = context.cents + pnl_cents
                print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "BUY", pnl_cents, context.cents)


        pattern_length = len(context.sell_pattern_to_match)
        if context.pattern[-pattern_length:] == context.sell_pattern_to_match:

            order(context.symbol, -10, limit_price = data[0]["open"])

            if context.take_profit_target and (data[0]["open"] - context.take_profit_target) >= data[0]["low"]:
                target_price = data[0]["open"] - context.take_profit_target
                order(context.symbol, 10, limit_price = target_price)
                pnl_cents = data[0]["open"] - target_price
                context.cents = context.cents + pnl_cents
                print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "SELL", pnl_cents, context.cents)
            else:
                order(context.symbol, 10, limit_price = data[0]["close"])
                pnl_cents = data[0]["open"] - data[0]["close"]
                context.cents = context.cents + pnl_cents
                print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "SELL", pnl_cents, context.cents)

        #pattern_length = len(context.follow_uptrend_pattern_to_match)
        #if context.pattern[-pattern_length:] == context.follow_uptrend_pattern_to_match:
        #
        #    order(context.symbol, 10, limit_price = data[0]["open"])
        #    order(context.symbol, -10, limit_price = data[0]["close"])
        #
        #    context.cents = context.cents + (data[0]["close"] - data[0]["open"])
        #    print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "FLW UPTREND BUY", data[0]["close"] - data[0]["open"], context.cents)

    context.prev_day_close_price = data[0]["close"]

    record(SMBL = data[0]["close"])

def handle_data_api(context, data):
    if context.incr == 0:
        assert 0 not in context.portfolio.positions
    else:
        assert context.portfolio.positions[0]["amount"] == context.incr, "Orders not filled immediately."
        assert context.portfolio.positions[0]["last_sale_price"] == data[0].price, "Orders not filled at current price."
    context.incr += 1
    order(0, 1)

    record(incr=context.incr)

def handle_data(context, data):
    context.cur_time += 1
    month = get_datetime().date().month
    is_january = (month == 1)

    new_prices = np.array([data[symbol(symbol_name)].price for symbol_name in context.symbols], dtype='float32')
    record(Prices=new_prices)
    new_prices = new_prices.reshape((context.N_STOCKS, 1))
    #     print context.returns_history.shape
    #     print new_prices.shape
    #     print context.previous_prices.shape
    context.returns_history = np.concatenate([context.returns_history, new_prices / context.previous_prices], axis=1)
    context.previous_prices = new_prices

    if context.month != month:
        # Trading in the beginning of month
        context.month_sizes.append(context.day_of_month)
        context.day_of_month = 1
        context.count_month += 1
        context.month_sizes.append(context.day_of_month)
        context.day_of_month = 1
        if context.count_month > N_MONTHS:
            # Deleting too old returns
            if context.count_month > N_MONTHS + 1:
                context.returns_history = np.delete(context.returns_history, range(context.month_sizes[-14]), axis=1)

            model_input = preprocess_data_for_model(context.returns_history, context.month_sizes[-13:], context.scaler)
            is_january_column = np.array([is_january] * context.N_STOCKS).reshape((context.N_STOCKS, 1))
            model_input = np.concatenate([is_january_column, model_input], axis=1)
            #             print 'Input shape', model_input.shape
            predicted_proba = context.model.predict_proba(model_input)
            #             print predicted_proba

            '''
            half_trade = len(context.symbols) * 1 / 10
            args_sorted = np.argsort(predicted_proba[:, 0])
            buy_args = args_sorted[:half_trade]
            sell_args = args_sorted[-half_trade:]

            for arg in buy_args:
                order_target(symbol(context.symbols[arg]), 1)
            for arg in sell_args:
                order_target(symbol(context.symbols[arg]), -1)
            '''
            for i in range(context.N_STOCKS):
                if predicted_proba[i, 0] > 0.5:
                    order_target(symbol(context.symbols[i]), 1)
                else:
                    order_target(symbol(context.symbols[i]), -1)
    else:
        context.day_of_month += 1

    context.month = month