I'm building my first algorithm with a very basic strategy.

I simply want to find all pharma companies with a market cap between [200M, 5B] with clinical result 6 month ahead of the current trading day and buy, then sell that position 1 month before the actual clinical trial result.

My current approach is to query the clinical trials data base and somehow combine that with a query to the fundamentals data base and I simply want to run through a unified dataframe to see what I should buy and what I should sell / hold.

My question would be: How to I combine my df result from the clinical trials database with that of the fundamentals and then loop through and buy / sell / hold?

I realize both have 'symbol' columns, just not sure how to leverage them.

If it is of any help here is my code so far, thank you for any input!

# for clinical trial data  
from quantopian.interactive.data.eventvestor import clinical_trials_free

# libraries for manipulating datasets  
from odo import odo  
import pandas as pd

# For building universe of stocks for algo  
from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline

# For getting fundamental data (primairly market cap data to filter companies)  
#from 

def initialize(context):  
    #set benchmark to XBI  
    set_benchmark(symbol('XBI'))  
    # Creates and attaches an empty pipeline  
    pipe = Pipeline()  
    pipe = attach_pipeline(pipe, name = "bio_pipeline")  
    # Find all sids in clinical dataset  
    upcoming = clinical_trials_free[clinical_trials_free.clinical_phase != "Phase I" and clinical_trials_free.clinical_phase != "Pre-Clinical" and clinical_trials_free.clinical_phase != "Phase 0" ][['symbol', 'sid','asof_date', 'clinical_phase', 'clinical_scope', 'clinical_result']]  
    upcoming_df = odo(upcoming, pd.DataFrame)  
    # Filter by market cap (<$5B USD)  
    lessThan5B_df = get_fundamentals(  
        query(  
            fundamentals.valuation.market_cap  
            fundamentals.share_class_reference.symbol  
        )  
        .filter(  
            fundamentals.share_class_reference.symbol in upcoming_df['symbol']  
        )  
        .filter(  
            fundamentals.valuation.market_cap <= 5000000000  
        )  
        .filter(fundamentals.valuation.market_cap >= 2000000  
        )  
    )  
    # Filter by "if event in next 6 months"

def before_trading_start(context, data):  
    pass  
    # results = pipeline_output("bio_pipeline")  
    # update_universe(results)

# Will be called on every trade event for the securities you specify.  
def handle_data(context, data):  
    pass