Hi everyone,

I want to backtest my own data using zipline, as we can't use history() and backtest for imported data.

I have got zipline installed but I can't find many examples to learn how to use zipline. Also the last two examples in zipline's example folder are not working. I want to find more detailed and comprehensive examples to learn writing algo using zipline.

Do you know where can I find more strategies examples of using zipline outside quantopian?

I have tried the following code to import my own data and calculate and plot indicators. However, as we can't use history() and backtest for imported data, so it won't work as I intended.

So, my request is that Could anyone help me to rewrite the code below (using zipline, not in quantopian) to achieve the following goals:

1. to import the data using the public link I provided and name it 'IF' (how to use fetcher.csv)
   
2. to calculate and plot RSI and SMA using history() on close prices of 'IF' data ( how to use history, how to plot signals)
   
3. to calculate and print the logbook of any simple strategy you write ( how to calc and print logbook of strategy)

import talib  
import numpy as np  
import math  
import pandas as pd

def initialize(context):  

    fetch_csv('https://copy.com/OWPM6TLGOZ8IlLj2',  
               date_column = 'Date',  
               date_format = '%Y/%m/%d',  
               pre_func = preview,  
               post_func = add_fields,  
               symbol='IF'  
             )   # used as signal  
    context.stock = symbol('IF')  ## I intend to use `history` and backtest to my own imported 'IF' data,  
                                                             ## but only symbol('IF') of quantopian can use history() and backtest  
    set_benchmark(symbol('IF'))  
##    set_commission()  
##    To set the cost of your trades, use the set_commission method and  
##    pass in PerShare or PerTrade.  
##    Like the slippage model, set_commission must be used in the initialize  
##    method and has no effect if used in handle_data.  
##    If you don't specify a commission,  
##    your backtest defaults to $0.03 per share.


def preview(df):  
    log.info(df['IF_close'].head())  
    return df  


def add_fields(df):   ## if you can calc indicator without history(), do it here  
    closes = df['IF_close']  
    fastMA = pd.rolling_mean(closes, 10)  
    df['fastMA'] = fastMA   # calc and add more signals to IF dataframe  
    slowMA = pd.rolling_mean(closes, 100)  
    df['slowMA'] = slowMA  
    macd = fastMA - slowMA  
    df['MACD'] = macd  

    log.info('\n%s ' % df.tail())  
    return df

def handle_data(context, data):  
    # close_price = history(1, '1d', 'IF_close')  
    # log.info(close_price[context.stock])  
    record(IF_close = data['IF']['IF_close'])  



    ## calc volumes' standard deviation  
    volumes = history(15, '1d', 'volume') ## select volume data of IF symbol  
    std = volumes.std()  
    std_data = std[context.stock]  
    ## calc RSI for close_price  
    prices = history(15, '1d', 'close_price') ## select close data  
    ## calc RSI for high_price  
    high_prices = history(15, '1d', 'high') ## select high data  
    rsi_data = prices.apply(talib.RSI, timeperiod=14).iloc[-1]  
    rsi_data_high = high_prices.apply(talib.RSI, timeperiod=14).iloc[-1]  


    IF_rsi = rsi_data[context.stock] # not [data['IF']['close']]  
    IF_rsi_high = rsi_data_high[context.stock] # not [data['IF']['close']]  
##    record(close = data['IF']['close']) # there can only be one extra plot area  
##    record(std = std_data)  
    # record(RSI = IF_rsi, RSI_high = IF_rsi_high)  
    record(fastMA = data['IF']['fastMA'])