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How to get data from N bars before?

I just want to get the data from N bars before. Basically want to know the high and low of a specific period. For example the first 2 hours.

I schedule a function and then how I can get the max and min value of those 2 hours of trading?

global maxim
for i in range(30):
if (self.Securities["SPY"].High[i] > maxim):
maxim= self.Securities["SPY"].High[i]

That doesn't work. I'm using a scheduled function in order to call this code block. But seems self.Securities["SPY"].High[i] is a float and not a list like I wanted.

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Hi Cristian,

We can get the data from N bars before and determine the min/max over the first 2 hours of the trading day using rolling windows.  In Initialize, we can create one window to track the previous 2 hours of candles and another window to track the first 2 hours of candles during the day.

self.rolling_window = RollingWindow[TradeBar](120) # 2 hour lookback
self.first_candles = RollingWindow[TradeBar](120) # First 2 hours in a trading day

On each new data slice in OnData, we add the new candle to rolling_window. We also add the new candle to first_candles if it is not already full.

self.rolling_window.Add(data['UBER'])
if not self.first_candles.IsReady:
self.first_candles.Add(data['UBER'])

To ensure the first_candles continues to represent the first 2 hours of candles in a trading day, we can reset it at the end of each day.

def OnEndOfDay(self):
self.first_candles.Reset()

Now, in OnData, we can determine the high/low of the candle 2 that occurred 2 hours ago

max_2_hrs_ago = self.rolling_window[self.rolling_window.Count-1].High
min_2_hrs_ago = self.rolling_window[self.rolling_window.Count-1].Low

We can also calculate the min/max of the first 2 hours of the trading day in OnData

min_first_2_hrs = float("inf")
max_first_2_hrs = 0
for candle in self.first_candles:
if candle.Low < min_first_2_hrs:
min_first_2_hrs = candle.Low
if candle.High > max_first_2_hrs:
max_first_2_hrs = candle.High

See the attached backtest for a full working example of this.

Best,
Derek

1

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Thank you so much Derek, sorry for not responding before, I was really busy with other things. Great explanation!

 

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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