Catalyst & Jupyter Notebook¶

(This is actual Notebook referenced in the text below)

The Jupyter Notebook is a very powerful browser-based interface to a Python interpreter. As it is already the de-facto interface for most quantitative researchers, catalyst provides an easy way to run your algorithm inside the Notebook without requiring you to use the CLI.

Install¶

In order to use Jupyter Notebook, you first have to install it inside your environment. It’s available as pip package, so regardless of how you installed Catalyst, go inside your catalyst environemnt and run:

(catalyst)$ pip install jupyter

Once you have Jupyter Notebook installed, every time you want to use it run:

(catalyst)$ jupyter notebook

A local server will launch, and will open a new window on your browser. That’s the interface through which you will interact with Jupyter Notebook.

Running Algorithms¶

Before running your algorithms inside the Jupyter Notebook, remember to ingest the data from the command line interface (CLI). In the example below, you would need to run first:

catalyst ingest-exchange -x bitfinex -i btc_usd

To use Catalyst inside a Jupyter Noebook, you have to write your algorithm in a cell and let the Jupyter know that it is supposed to execute this algorithm with Catalyst. This is done via the %%catalyst IPython magic command that is available after you import catalyst from within the Notebook. This magic takes the same arguments as the command line interface. Thus to run the algorithm just supply the same parameters as the CLI but without the -f and -o arguments. We just have to execute the following cell after importing catalyst to register the magic.

# Register the catalyst magic
%load_ext catalyst

# Setup matplotlib to display graphs inline in this Notebook
%matplotlib inline

Note below that we do not have to specify an input file (-f) since the magic will use the contents of the cell and look for your algorithm functions.

%%catalyst --start 2015-3-2 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd

from catalyst.finance.slippage import VolumeShareSlippage

from catalyst.api import (
    order_target_value,
    symbol,
    record,
    cancel_order,
    get_open_orders,
)

def initialize(context):
    context.ASSET_NAME = 'btc_usd'
    context.TARGET_HODL_RATIO = 0.8
    context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO

    # For all trading pairs in the poloniex bundle, the default denomination
    # currently supported by Catalyst is 1/1000th of a full coin. Use this
    # constant to scale the price of up to that of a full coin if desired.
    context.TICK_SIZE = 1000.0

    context.is_buying = True
    context.asset = symbol(context.ASSET_NAME)

    context.i = 0

def handle_data(context, data):
    context.i += 1

    starting_cash = context.portfolio.starting_cash
    target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
    reserve_value = context.RESERVE_RATIO * starting_cash

    # Cancel any outstanding orders
    orders = get_open_orders(context.asset) or []
    for order in orders:
        cancel_order(order)

    # Stop buying after passing the reserve threshold
    cash = context.portfolio.cash
    if cash <= reserve_value:
        context.is_buying = False

    # Retrieve current asset price from pricing data
    price = data.current(context.asset, 'price')

    # Check if still buying and could (approximately) afford another purchase
    if context.is_buying and cash > price:
        # Place order to make position in asset equal to target_hodl_value
        order_target_value(
            context.asset,
            target_hodl_value,
            limit_price=price*1.1,
            stop_price=price*0.9,
        )

    record(
        price=price,
        volume=data.current(context.asset, 'volume'),
        cash=cash,
        starting_cash=context.portfolio.starting_cash,
        leverage=context.account.leverage,
    )

def analyze(context=None, results=None):
    import matplotlib.pyplot as plt

    # Plot the portfolio and asset data.
    ax1 = plt.subplot(611)
    results[['portfolio_value']].plot(ax=ax1)
    ax1.set_ylabel('Portfolio Value (USD)')

    ax2 = plt.subplot(612, sharex=ax1)
    ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
    (context.TICK_SIZE * results[['price']]).plot(ax=ax2)

    trans = results.ix[[t != [] for t in results.transactions]]
    buys = trans.ix[
        [t[0]['amount'] > 0 for t in trans.transactions]
    ]
    ax2.plot(
        buys.index,
        context.TICK_SIZE * results.price[buys.index],
        '^',
        markersize=10,
        color='g',
    )

    ax3 = plt.subplot(613, sharex=ax1)
    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
    ax3.set_ylabel('Leverage ')

    ax4 = plt.subplot(614, sharex=ax1)
    results[['starting_cash', 'cash']].plot(ax=ax4)
    ax4.set_ylabel('Cash (USD)')

    results[[
        'treasury',
        'algorithm',
        'benchmark',
    ]] = results[[
        'treasury_period_return',
        'algorithm_period_return',
        'benchmark_period_return',
    ]]

    ax5 = plt.subplot(615, sharex=ax1)
    results[[
        'treasury',
        'algorithm',
        'benchmark',
    ]].plot(ax=ax5)
    ax5.set_ylabel('Percent Change')

    ax6 = plt.subplot(616, sharex=ax1)
    results[['volume']].plot(ax=ax6)
    ax6.set_ylabel('Volume (mCoins/5min)')

    plt.legend(loc=3)

    # Show the plot.
    plt.gcf().set_size_inches(18, 8)
    plt.show()

[2017-08-11 07:19:46.411748] INFO: Loader: Loading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00
[2017-08-11 07:19:46.418983] INFO: Loader: Loading data for /Users/<snipped>/.catalyst/data/USDT_BTC_benchmark.csv failed with error [Unknown string format].
[2017-08-11 07:19:46.419740] INFO: Loader: Cache at /Users/<snipped>/.catalyst/data/USDT_BTC_benchmark.csv does not have data from 1990-01-01 00:00:00+00:00 to 2017-08-09 00:00:00+00:00.

[2017-08-11 07:19:46.420770] INFO: Loader: Downloading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00
[2017-08-11 07:19:50.060244] WARNING: Loader: Still don't have expected data after redownload!
[2017-08-11 07:19:50.097334] WARNING: Loader: Refusing to download new treasury data because a download succeeded at 2017-08-11 06:56:49+00:00.
[2017-08-11 07:19:54.618399] INFO: Performance: Simulated 851 trading days out of 851.
[2017-08-11 07:19:54.619301] INFO: Performance: first open: 2015-03-01 00:00:00+00:00
[2017-08-11 07:19:54.620430] INFO: Performance: last close: 2017-06-28 23:59:00+00:00

	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	cash	ending_cash	ending_exposure	…	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return	volume	treasury	algorithm	benchmark
2015-03-01 23:59:00+00:00	NaN	0.000000	NaN	0.045833	NaN	NaN	0.000000	100000.000000	100000.000000	0.000	…	100000.0	0.000	0.000	1	[]	0.0200	317	0.0200	0.000000	0.045833
2015-03-02 23:59:00+00:00	0.000278	-0.000025	0.011045	0.120833	0.290503	-0.000956	-85544.474955	14455.525045	14455.525045	85542.000	…	100000.0	0.000	0.000	2	[{u’commission’: None, u’amount’: 318, u’sid’:…	0.0208	98063	0.0208	-0.000025	0.120833
2015-03-03 23:59:00+00:00	0.051796	-0.005688	-1.197544	0.113416	0.633538	0.077239	0.000000	14455.525045	14455.525045	84975.642	…	100000.0	85542.000	85542.000	3	[]	0.0212	442983	0.0212	-0.005688	0.113416
2015-03-04 23:59:00+00:00	0.342118	0.034955	0.401861	0.166666	0.524400	0.181468	0.000000	14455.525045	14455.525045	89040.000	…	100000.0	84975.642	84975.642	4	[]	0.0212	245889	0.0212	0.034955	0.166666
2015-03-05 23:59:00+00:00	0.637226	-0.038185	-3.914003	0.070834	0.976896	0.550520	0.000000	14455.525045	14455.525045	81726.000	…	100000.0	89040.000	89040.000	5	[]	0.0211	117440	0.0211	-0.038185	0.070834
2015-03-06 23:59:00+00:00	0.580521	-0.028645	-3.100822	0.083333	0.874082	0.546703	0.000000	14455.525045	14455.525045	82680.000	…	100000.0	81726.000	81726.000	6	[]	0.0224	84197	0.0224	-0.028645	0.083333
2015-03-07 23:59:00+00:00	0.530557	-0.028645	-2.625704	0.083333	0.802793	0.536589	0.000000	14455.525045	14455.525045	82680.000	…	100000.0	82680.000	82680.000	7	[]	0.0224	181	0.0224	-0.028645	0.083333
2015-03-08 23:59:00+00:00	0.491628	-0.028645	-2.276841	0.083333	0.746605	0.529163	0.000000	14455.525045	14455.525045	82680.000	…	100000.0	82680.000	82680.000	8	[]	0.0224	30900	0.0224	-0.028645	0.083333
2015-03-09 23:59:00+00:00	0.467885	-0.015925	-1.895269	0.100000	0.698764	0.532652	0.000000	14455.525045	14455.525045	83952.000	…	100000.0	82680.000	82680.000	9	[]	0.0220	128367	0.0220	-0.015925	0.100000
2015-03-10 23:59:00+00:00	0.626552	0.069935	-1.625285	0.212500	0.800983	0.676289	0.000000	14455.525045	14455.525045	92538.000	…	100000.0	83952.000	83952.000	10	[]	0.0214	54961	0.0214	0.069935	0.212500
2015-03-11 23:59:00+00:00	0.644515	0.022235	-1.727710	0.150000	0.834650	0.684052	0.000000	14455.525045	14455.525045	87768.000	…	100000.0	92538.000	92538.000	11	[]	0.0211	42511	0.0211	0.022235	0.150000
2015-03-12 23:59:00+00:00	0.614650	0.022235	-1.573455	0.150000	0.798403	0.680882	0.000000	14455.525045	14455.525045	87768.000	…	100000.0	87768.000	87768.000	12	[]	0.0210	2909	0.0210	0.022235	0.150000
2015-03-13 23:59:00+00:00	0.588942	0.019405	-1.454733	0.146291	0.767688	0.677881	0.000000	14455.525045	14455.525045	87484.980	…	100000.0	87768.000	87768.000	13	[]	0.0213	57613	0.0213	0.019405	0.146291
2015-03-14 23:59:00+00:00	0.565911	0.019373	-1.344915	0.146250	0.739230	0.675665	0.000000	14455.525045	14455.525045	87481.800	…	100000.0	87484.980	87484.980	14	[]	0.0213	48310	0.0213	0.019373	0.146250
2015-03-15 23:59:00+00:00	0.551394	0.041659	-1.191436	0.175450	0.714876	0.680484	0.000000	14455.525045	14455.525045	89710.344	…	100000.0	87481.800	87481.800	15	[]	0.0213	29454	0.0213	0.041659	0.175450
2015-03-16 23:59:00+00:00	0.541846	0.019055	-1.188212	0.145833	0.706049	0.680281	0.000000	14455.525045	14455.525045	87450.000	…	100000.0	89710.344	89710.344	16	[]	0.0210	25564	0.0210	0.019055	0.145833
2015-03-17 23:59:00+00:00	0.524682	0.019055	-1.115149	0.145833	0.684599	0.678870	0.000000	14455.525045	14455.525045	87450.000	…	100000.0	87450.000	87450.000	17	[]	0.0206	9	0.0206	0.019055	0.145833
2015-03-18 23:59:00+00:00	0.532621	-0.021999	-1.180440	0.092041	0.696261	0.685307	0.000000	14455.525045	14455.525045	83344.620	…	100000.0	87450.000	87450.000	18	[]	0.0193	164911	0.0193	-0.021999	0.092041
2015-03-19 23:59:00+00:00	0.518811	-0.013234	-1.096387	0.103526	0.676861	0.686186	0.000000	14455.525045	14455.525045	84221.028	…	100000.0	83344.620	83344.620	19	[]	0.0198	713904	0.0198	-0.013234	0.103526
2015-03-20 23:59:00+00:00	0.505168	-0.017324	-1.050273	0.098170	0.659945	0.685070	0.000000	14455.525045	14455.525045	83812.080	…	100000.0	84221.028	84221.028	20	[]	0.0193	132725	0.0193	-0.017324	0.098170
2015-03-21 23:59:00+00:00	0.492384	-0.018494	-1.002051	0.096637	0.643679	0.684283	0.000000	14455.525045	14455.525045	83695.056	…	100000.0	83812.080	83812.080	21	[]	0.0193	201155	0.0193	-0.018494	0.096637
2015-03-22 23:59:00+00:00	0.482998	-0.004744	-0.927947	0.114653	0.629319	0.686478	0.000000	14455.525045	14455.525045	85070.088	…	100000.0	83695.056	83695.056	22	[]	0.0193	64378	0.0193	-0.004744	0.114653
2015-03-23 23:59:00+00:00	0.477523	-0.026505	-0.935352	0.086139	0.623502	0.687025	0.000000	14455.525045	14455.525045	82894.014	…	100000.0	85070.088	85070.088	23	[]	0.0192	61850	0.0192	-0.026505	0.086139
2015-03-24 23:59:00+00:00	0.504086	-0.084215	-1.021023	0.010523	0.655188	0.701025	0.000000	14455.525045	14455.525045	77122.950	…	100000.0	82894.014	82894.014	24	[]	0.0188	490180	0.0188	-0.084215	0.010523
2015-03-25 23:59:00+00:00	0.497690	-0.068474	-0.952786	0.031148	0.644272	0.704251	0.000000	14455.525045	14455.525045	78697.050	…	100000.0	77122.950	77122.950	25	[]	0.0193	90862	0.0193	-0.068474	0.031148
2015-03-26 23:59:00+00:00	0.489730	-0.084215	-0.943240	0.010523	0.634965	0.703738	0.000000	14455.525045	14455.525045	77122.950	…	100000.0	78697.050	78697.050	26	[]	0.0201	2299	0.0201	-0.084215	0.010523
2015-03-27 23:59:00+00:00	0.495916	-0.049785	-0.857592	0.055636	0.636644	0.713671	0.000000	14455.525045	14455.525045	80565.936	…	100000.0	77122.950	77122.950	27	[]	0.0195	663	0.0195	-0.049785	0.055636
2015-03-28 23:59:00+00:00	0.488469	-0.064490	-0.848769	0.036368	0.627920	0.713212	0.000000	14455.525045	14455.525045	79095.504	…	100000.0	80565.936	80565.936	28	[]	0.0195	7061	0.0195	-0.064490	0.036368
2015-03-29 23:59:00+00:00	0.479671	-0.066903	-0.822844	0.033205	0.616787	0.712868	0.000000	14455.525045	14455.525045	78854.142	…	100000.0	79095.504	79095.504	29	[]	0.0195	8526	0.0195	-0.066903	0.033205
2015-03-30 23:59:00+00:00	0.476306	-0.046605	-0.769239	0.059803	0.610002	0.716464	0.000000	14455.525045	14455.525045	80883.936	…	100000.0	78854.142	78854.142	30	[]	0.0196	29654	0.0196	-0.046605	0.059803
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
2017-05-30 23:59:00+00:00	0.495432	5.949752	-0.016611	7.916664	0.554369	0.888883	0.000000	14455.525045	14455.525045	680519.682	…	100000.0	701826.000	701826.000	822	[]	0.0221	40157964723	0.0221	5.949752	7.916664
2017-05-31 23:59:00+00:00	0.495243	6.102328	-0.017086	8.154164	0.554182	0.888844	0.000000	14455.525045	14455.525045	695777.322	…	100000.0	680519.682	680519.682	823	[]	0.0221	31098652109	0.0221	6.102328	8.154164
2017-06-01 23:59:00+00:00	0.495836	6.504967	-0.014668	8.644144	0.554541	0.889303	0.000000	14455.525045	14455.525045	736041.210	…	100000.0	695777.322	695777.322	824	[]	0.0221	40944880757	0.0221	6.504967	8.644144
2017-06-02 23:59:00+00:00	0.495948	6.801995	-0.013641	9.033331	0.554581	0.889440	0.000000	14455.525045	14455.525045	765744.000	…	100000.0	736041.210	736041.210	825	[]	0.0215	22364557424	0.0215	6.801995	9.033331
2017-06-03 23:59:00+00:00	0.495729	6.952409	-0.013100	9.230418	0.554317	0.889470	0.000000	14455.525045	14455.525045	780785.400	…	100000.0	765744.000	765744.000	826	[]	0.0215	23687278961	0.0215	6.952409	9.230418
2017-06-04 23:59:00+00:00	0.495450	7.042244	-0.012768	9.348122	0.553999	0.889479	0.000000	14455.525045	14455.525045	789768.900	…	100000.0	780785.400	780785.400	827	[]	0.0215	21332021248	0.0215	7.042244	9.348122
2017-06-05 23:59:00+00:00	0.496148	7.524987	-0.011320	9.980649	0.554578	0.889805	0.000000	14455.525045	14455.525045	838043.208	…	100000.0	789768.900	789768.900	828	[]	0.0218	22372229837	0.0218	7.524987	9.980649
2017-06-06 23:59:00+00:00	0.497592	8.194835	-0.009554	10.858330	0.555841	0.890368	0.000000	14455.525045	14455.525045	905028.000	…	100000.0	838043.208	838043.208	829	[]	0.0214	81923184446	0.0214	8.194835	10.858330
2017-06-07 23:59:00+00:00	0.498895	7.557258	-0.011975	10.022932	0.557003	0.890845	0.000000	14455.525045	14455.525045	841270.272	…	100000.0	905028.000	905028.000	830	[]	0.0218	49070430356	0.0218	7.557258	10.022932
2017-06-08 23:59:00+00:00	0.499349	8.010395	-0.010676	10.616664	0.557357	0.891092	0.000000	14455.525045	14455.525045	886584.000	…	100000.0	841270.272	841270.272	831	[]	0.0219	34013412940	0.0219	8.010395	10.616664
2017-06-09 23:59:00+00:00	0.499063	8.099750	-0.010386	10.733746	0.557033	0.891098	0.000000	14455.525045	14455.525045	895519.482	…	100000.0	886584.000	886584.000	832	[]	0.0221	25275425996	0.0221	8.099750	10.733746
2017-06-10 23:59:00+00:00	0.498769	8.086143	-0.010416	10.715915	0.556705	0.891098	0.000000	14455.525045	14455.525045	894158.760	…	100000.0	895519.482	895519.482	833	[]	0.0221	30620792046	0.0221	8.086143	10.715915
2017-06-11 23:59:00+00:00	0.498971	8.484533	-0.009305	11.237914	0.556827	0.891266	0.000000	14455.525045	14455.525045	933997.800	…	100000.0	894158.760	894158.760	834	[]	0.0221	30830678595	0.0221	8.484533	11.237914
2017-06-12 23:59:00+00:00	0.503448	7.320494	-0.014065	9.712706	0.560936	0.892695	0.000000	14455.525045	14455.525045	817593.900	…	100000.0	933997.800	933997.800	835	[]	0.0221	88704710635	0.0221	7.320494	9.712706
2017-06-13 23:59:00+00:00	0.503565	7.656697	-0.013054	10.153225	0.560981	0.892830	0.000000	14455.525045	14455.525045	851214.132	…	100000.0	817593.900	817593.900	836	[]	0.0221	42251296767	0.0221	7.656697	10.153225
2017-06-14 23:59:00+00:00	0.506845	6.734516	-0.016873	8.944917	0.563995	0.893862	0.000000	14455.525045	14455.525045	758996.040	…	100000.0	851214.132	851214.132	837	[]	0.0215	63183088135	0.0215	6.734516	8.944917
2017-06-15 23:59:00+00:00	0.506562	6.695367	-0.016991	8.893622	0.563678	0.893865	0.000000	14455.525045	14455.525045	755081.142	…	100000.0	758996.040	758996.040	838	[]	0.0216	104677533974	0.0216	6.695367	8.893622
2017-06-16 23:59:00+00:00	0.506404	6.887855	-0.016343	9.145831	0.563472	0.893913	0.000000	14455.525045	14455.525045	774330.000	…	100000.0	755081.142	755081.142	839	[]	0.0216	43479966625	0.0216	6.887855	9.145831
2017-06-17 23:59:00+00:00	0.507407	7.435283	-0.014812	9.863113	0.564341	0.894311	0.000000	14455.525045	14455.525045	829072.746	…	100000.0	774330.000	774330.000	840	[]	0.0216	36800919715	0.0216	7.435283	9.863113
2017-06-18 23:59:00+00:00	0.507740	7.070069	-0.016112	9.384581	0.564605	0.894482	0.000000	14455.525045	14455.525045	792551.400	…	100000.0	829072.746	829072.746	841	[]	0.0216	46411759478	0.0216	7.070069	9.384581
2017-06-19 23:59:00+00:00	0.507754	7.358645	-0.015226	9.762694	0.564557	0.894583	0.000000	14455.525045	14455.525045	821408.946	…	100000.0	792551.400	792551.400	842	[]	0.0219	28294406623	0.0219	7.358645	9.762694
2017-06-20 23:59:00+00:00	0.507705	7.628795	-0.014414	10.116664	0.564451	0.894665	0.000000	14455.525045	14455.525045	848424.000	…	100000.0	821408.946	821408.946	843	[]	0.0216	36903854052	0.0216	7.628795	10.116664
2017-06-21 23:59:00+00:00	0.507531	7.476155	-0.014900	9.916664	0.564238	0.894696	0.000000	14455.525045	14455.525045	833160.000	…	100000.0	848424.000	848424.000	844	[]	0.0216	43815656010	0.0216	7.476155	9.916664
2017-06-22 23:59:00+00:00	0.507315	7.645891	-0.014372	10.139065	0.563979	0.894725	0.000000	14455.525045	14455.525045	850133.568	…	100000.0	833160.000	833160.000	845	[]	0.0215	22304647568	0.0215	7.645891	10.139065
2017-06-23 23:59:00+00:00	0.507020	7.635155	-0.014388	10.124997	0.563652	0.894725	0.000000	14455.525045	14455.525045	849060.000	…	100000.0	850133.568	850133.568	846	[]	0.0215	13090231864	0.0215	7.635155	10.124997
2017-06-24 23:59:00+00:00	0.507936	7.105628	-0.016304	9.431173	0.564463	0.895061	0.000000	14455.525045	14455.525045	796107.276	…	100000.0	849060.000	849060.000	847	[]	0.0215	34088563732	0.0215	7.105628	9.431173
2017-06-25 23:59:00+00:00	0.507675	7.036714	-0.016515	9.340880	0.564168	0.895069	0.000000	14455.525045	14455.525045	789215.898	…	100000.0	796107.276	796107.276	848	[]	0.0215	41560204433	0.0215	7.036714	9.340880
2017-06-26 23:59:00+00:00	0.507780	6.761571	-0.017485	8.980368	0.564221	0.895175	0.000000	14455.525045	14455.525045	761701.584	…	100000.0	789215.898	789215.898	849	[]	0.0214	73840480752	0.0214	6.761571	8.980368
2017-06-27 23:59:00+00:00	0.508048	7.126355	-0.016390	9.458331	0.564409	0.895349	0.000000	14455.525045	14455.525045	798180.000	…	100000.0	761701.584	761701.584	850	[]	0.0221	62426319778	0.0221	7.126355	9.458331
2017-06-28 23:59:00+00:00	0.507750	7.135895	-0.016340	9.470831	0.564078	0.895349	0.000000	14455.525045	14455.525045	799134.000	…	100000.0	798180.000	798180.000	851	[]	0.0222	39676839183	0.0222	7.135895	9.470831

851 rows × 45 columns

Also, instead of defining an output file we are accessing it via the “_” variable that will be created in the name space and contain the performance DataFrame.

_.head()

	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	cash	ending_cash	ending_exposure	…	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return	volume	treasury	algorithm	benchmark
2015-03-01 23:59:00+00:00	NaN	0.000000	NaN	0.045833	NaN	NaN	0.000000	100000.000000	100000.000000	0.000	…	100000.0	0.000	0.000	1	[]	0.0200	317	0.0200	0.000000	0.045833
2015-03-02 23:59:00+00:00	0.000278	-0.000025	0.011045	0.120833	0.290503	-0.000956	-85544.474955	14455.525045	14455.525045	85542.000	…	100000.0	0.000	0.000	2	[{u’commission’: None, u’amount’: 318, u’sid’:…	0.0208	98063	0.0208	-0.000025	0.120833
2015-03-03 23:59:00+00:00	0.051796	-0.005688	-1.197544	0.113416	0.633538	0.077239	0.000000	14455.525045	14455.525045	84975.642	…	100000.0	85542.000	85542.000	3	[]	0.0212	442983	0.0212	-0.005688	0.113416
2015-03-04 23:59:00+00:00	0.342118	0.034955	0.401861	0.166666	0.524400	0.181468	0.000000	14455.525045	14455.525045	89040.000	…	100000.0	84975.642	84975.642	4	[]	0.0212	245889	0.0212	0.034955	0.166666
2015-03-05 23:59:00+00:00	0.637226	-0.038185	-3.914003	0.070834	0.976896	0.550520	0.000000	14455.525045	14455.525045	81726.000	…	100000.0	89040.000	89040.000	5	[]	0.0211	117440	0.0211	-0.038185	0.070834

5 rows × 45 columns