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Help with creating a data series of differences

Which way is the most elegant way to create a series of differences on PMI data. How do i get a change in the PMI data istead of the PMI data it self?

              symbol = "PMI",  
              date_column = "Date",  
              date_format = "%d/$m/%Y")  

def my_rebalance(context, data):
PMI = data.current("PMI","Index")
weights= 0
if PMI >50:
weights = ((float(1)/len(context.longs)))
for stock in context.longs.index:
if stock in context.longs.index and data.can_trade(stock):
order_target_percent(stock, weights)

for stock in context.portfolio.positions:  
    if stock not in context.longs.index and data.can_trade(stock) and PMI:  
        order_target_percent(stock, 0)``  
1 response

Hi Soren,

The best way to do it would be to pre-process your data using the pre_func parameter of fetch_csv. This parameter allows you to specify a function that takes in your imported data as a DataFrame, preforms some pre-processing logic and returns it to fetch_csv.

The example below takes in your PMI data, calculates the percentage change and appends it as a new column to your imported data.

I hope this helps.

Clone Algorithm
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
def initialize(context):
        symbol = "PMI",
        date_column = "Date",
        date_format = "%d/$m/%Y")
def preview(df):
    return df

def pct_change(df):
    df['pct_change'] = (df.Index - df.Index.shift(-1)) / df.Index.shift(-1)
    return df

def before_trading_start(context, data):  
There was a runtime error.

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