New Bar type: Dollar Bars

[artson]

Hi,

I think you don't have it (yet), but, have you thought about creating Dollar Bars?
Would it make sense?

https://quant.stackexchange.com/questions/43534/volume-or-dollar-bars-vs-volatility-normalized-and-demeaned-financial-time-seri

[NinjaTrader_ChelseaB]

Hello artson,

Yes you are able to create a custom bar type.

Below is a link to some custom bar types in the User App Share.

https://ninjatraderecosystem.com/user-app-share-results/?fwp_usa_search=bar+type

Are you planning to multiply the volume by the market price and accumulate this?

[artson]

Thanks Chelsea!

Yes, this is the idea

In this paper I sent, it seems to be a better "price approach"
Also it would make easier to compare 2 different instruments (ES, gold, oil.....)

It's a first idea

[NinjaTrader_ChelseaB]

Hello artson,

I should also have included a to the help guide, which I am including below.

https://ninjatrader.com/support/helpGuides/nt8/bars_type.htm

Let me know if you have any questions as you develop your custom bar type.

You can also contact a professional NinjaScript Consultant who would be eager to create or modify this script at your request or assist you with your script. The NinjaTrader Ecosystem has affiliate contacts who provide educational as well as consulting services. Please let me know if you would like our NinjaTrader Ecosystem team follow up with you with a list of affiliate consultants who would be happy to create this script or any others at your request or provide one on one educational services.

[mazpat_3]

artson any progress on this new bar type? Thanks

[Ymcapital]

Curious what happened with this or if anyone created dollar bars, tick imbalance bars, or tick run bars. Below are two Python snippets of code for tick imbalance bars and the other code is for tick run bars. Not the exact same thing as dollar bars but it offers up another way to consider visualizing the data.

## code to create tick imbalance bar
weighted_sum_T = 7
alpha = 0.9
weighted_sum_prob = 0.3
data["delta_p"] = data['open'].pct_change()
imbalance = 2
b_t = np.zeros(len(data["delta_p"]))
b_t[0]=1
indx = []
T=0

for i in range(1,len(data["delta_p"])):
if data["delta_p"][i] == 0:
b_t[i]=b_t[i-1]
else:
b_t[i] = np.abs(data["delta_p"][i])/data["delta_p"][i]
T+=1
if (abs(b_t.sum())>=imbalance):
indx.append(i)
weighted_sum_T = alpha*T + (1-alpha)*weighted_sum_T ### E(T)
weighted_sum_prob = alpha*sum(x>0 for x in b_t)/(T) + (1 - alpha) * weighted_sum_prob
imbalance = weighted_sum_T * abs(2*weighted_sum_prob-1)

T = 0
b_t = np.zeros(len(data["delta_p"]))



final_data=pd.DataFrame()
open =[]
close=[]
volume=[]
for i in range(len(indx)-1):
ind = (indx[i],indx[i+1])
temp_data = data[ind[0]:ind[1]]
open.append(temp_data['open'].iloc[0])
close.append(temp_data['close'].iloc[-1])
volume.append(temp_data['volume'].sum())

final_data['open']=open
final_data['close']=close
final_data['volume']=volume

Tick Run Bars:

weighted_sum_T = 7
alpha = 0.9
weighted_sum_prob = 0.3
data["delta_p"] = data['open'].pct_change()
imbalance = 2
b_t = np.zeros(len(data["delta_p"]))
b_t[0]=1
indx = []
T=0

for i in range(1,len(data["delta_p"])):
if data["delta_p"][i] == 0:
b_t[i]=b_t[i-1]
else:
b_t[i] = np.abs(data["delta_p"][i])/data["delta_p"][i]
T+=1
theta = max(sum(x>0 for x in b_t), -sum(x<0 for x in b_t))
if (theta >=imbalance):
indx.append(i)
weighted_sum_T = alpha*T + (1-alpha)*weighted_sum_T
weighted_sum_prob = alpha*sum(x>0 for x in b_t)/(T) + (1 - alpha) * weighted_sum_prob
imbalance = weighted_sum_T * max(1-weighted_sum_prob, weighted_sum_prob)

T = 0
b_t = np.zeros(len(data["delta_p"]))

final_data=pd.DataFrame()
open =[]
close=[]
volume=[]
for i in range(len(indx)-1):
ind = (indx[i],indx[i+1])
temp_data = data[ind[0]:ind[1]]
open.append(temp_data['open'].iloc[0])
close.append(temp_data['close'].iloc[-1])
volume.append(temp_data['volume'].sum())

final_data['open']=open
final_data['close']=close
final_data['volume']=volume