Hello community, I have a self-serve data that I want to use in my algo. Below is the top of the data:

date             ticker          score  
12/3/15 MTSC    0.538719768  
12/4/15 WAFD    -0.501787895  
12/9/15 HZO 0.501683899  

I made a pipeline to pass the latest data (current day) in before_trading_start function and assign that to context.output, and I have a print statement in my rebalance function which happens every day.

Start from 12/3/15, it's supposed to be 1 record, 1 record, and 0 record until 12/9/15. However, what I got from the print is as following:

2015-12-03 09:35  PRINT                        score  
Equity(5107 [MTSC])  0.53872  
2015-12-04 09:35  PRINT                         score  
Equity(5107 [MTSC])  0.538720  
Equity(8162 [WAFD]) -0.501788  
2015-12-07 09:35  PRINT                         score  
Equity(5107 [MTSC])  0.538720  
Equity(8162 [WAFD]) -0.501788  
2015-12-08 09:35  PRINT                         score  
Equity(5107 [MTSC])  0.538720  
Equity(8162 [WAFD]) -0.501788  
2015-12-09 09:35  PRINT                         score  
Equity(5107 [MTSC])  0.538720  
Equity(8162 [WAFD]) -0.501788  
Equity(18917 [HZO])  0.501684  

Looks like the pipeline output is a cumulated instead of what I described above. Did I do something wrong?

This is the code that I used:

import quantopian.algorithm as algo  
import pandas as pd  
import numpy as np  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.filters import QTradableStocksUS  
from quantopian.pipeline.data import placeholder as sample

def initialize(context):  
    schedule_function(my_rebalance,  
                      date_rules.every_day(),  
                      time_rules.market_open(hours = 0, minutes = 5))  
    algo.attach_pipeline(make_pipeline(), 'my_pipeline')  
def make_pipeline():  
    base_universe = QTradableStocksUS()  
    pipe = Pipeline(  
        columns = {  
            'score': sample.score.latest  
        },  
        screen = (base_universe & sample.score.latest.notnull())  
    )  
    return pipe

def before_trading_start(context, data):  
    context.output = algo.pipeline_output('my_pipeline')  
def my_rebalance(context, data):  
    # Get current date data  
    df = context.output.copy()  
    print(df)