From d1faac04161e0f7b74228d681f224e93e5598da0 Mon Sep 17 00:00:00 2001 From: Victor Grau Serrat Date: Mon, 27 Nov 2017 21:07:57 -0700 Subject: [PATCH] DOC: improved beginner tutorial --- _sources/beginner-tutorial.txt | 299 +++++++++++++++++++++++-------- appendix.html | 2 +- beginner-tutorial.html | 317 +++++++++++++++++++++++---------- bundles.html | 31 ++-- development-guidelines.html | 31 ++-- example-algos.html | 31 ++-- genindex.html | 4 +- index.html | 2 +- install.html | 31 ++-- jupyter.html | 31 ++-- live-trading.html | 31 ++-- naming-convention.html | 31 ++-- release-process.html | 31 ++-- releases.html | 2 +- resources.html | 31 ++-- search.html | 4 +- searchindex.js | 2 +- utilities.html | 31 ++-- videos.html | 31 ++-- welcome.html | 31 ++-- 20 files changed, 617 insertions(+), 387 deletions(-) diff --git a/_sources/beginner-tutorial.txt b/_sources/beginner-tutorial.txt index f79c2c7e..6743f82d 100644 --- a/_sources/beginner-tutorial.txt +++ b/_sources/beginner-tutorial.txt @@ -159,13 +159,17 @@ You can now test your algorithm using cryptoassets' historical pricing data, ``catalyst`` provides three interfaces: - A command-line interface (CLI), -- the ``IPython Notebook`` magic, -- and a :func:`~catalyst.run_algorithm` that you can call from other - Python scripts. +- a :func:`~catalyst.run_algorithm()` that you can call from other + Python scripts, +- and the ``Jupyter Notebook`` magic. -We'll start with the CLI, and introduce the ``IPython Notebook`` below. Some of -the :doc:`example algorithms ` provide instructions on how to run -them both from the CLI, and using the :func:`~catalyst.run_algorithm` function. + +We'll start with the CLI, and introduce the ``run_algorithm()`` in the last +example of this tutorial. Some of the :doc:`example algorithms ` +provide instructions on how to run them both from the CLI, and using the +:func:`~catalyst.run_algorithm` function. For the third method, refer to the +corresponding section on :doc:`Catalyst & Jupyter Notebook ` after you +have assimilated the contents of this tutorial. Command line interface ^^^^^^^^^^^^^^^^^^^^^^ @@ -263,7 +267,7 @@ command line args all the time. Thus, to execute our algorithm from above and save the results to ``buy_btc_simple_out.pickle`` we would call ``catalyst run`` as follows: -.. code-block:: python +.. code-block:: bash catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -c usd --capital-base 100000 -o buy_btc_simple_out.pickle @@ -560,78 +564,235 @@ If the short-mavg crosses from above we exit the positions as we assume the stock to go down further. As we need to have access to previous prices to implement this strategy -we need a new concept: History +we need a new concept: History. ``data.history()`` is a convenience function +that keeps a rolling window of data for you. The first argument is the number +of bars you want to collect, the second argument is the unit (either ``'1d'`` +for daily or ``'1m'`` for minute frequency, but note that you need to have +minute-level data when using ``1m``). This is a function we use in the +``handle_data()`` section. -``data.history()`` is a convenience function that keeps a rolling window of -data for you. The first argument is the number of bars you want to -collect, the second argument is the unit (either ``'1d'`` for ``'1m'`` -but note that you need to have minute-level data for using ``1m``). This is -a function we use in the ``handle_data()`` section: - -.. code-block:: python - - %load_ext catalyst +You will note that the code below is substantially longer than the previous +examples. Don't get overwhelmed by it as the logic is fairly simple and easy to +follow. Most of the added some complexity has been added to beautify the output, +which you can skim through for now. A copy of this algorithm is available in +the ``examples`` directory: +`dual_moving_average.py `_. .. code-block:: python - %%catalyst --start 2016-4-1 --end 2017-9-30 -x bitfinex + import numpy as np + import pandas as pd + from logbook import Logger + import matplotlib.pyplot as plt - from catalyst.api import order, record, symbol, order_target + from catalyst import run_algorithm + from catalyst.api import (order, record, symbol, order_target_percent, + get_open_orders) + from catalyst.exchange.stats_utils import extract_transactions + + NAMESPACE = 'dual_moving_average' + log = Logger(NAMESPACE) def initialize(context): - context.i = 0 - context.asset = symbol('btc_usd') + context.i = 0 + context.asset = symbol('ltc_usd') + context.base_price = None + def handle_data(context, data): - # Skip first 150 days to get full windows - context.i += 1 - if context.i < 150: + # define the windows for the moving averages + short_window = 50 + long_window = 200 + + # Skip as many bars as long_window to properly compute the average + context.i += 1 + if context.i < long_window: return - # Compute averages - # data.history() has to be called with the same params - # from above and returns a pandas dataframe. - short_mavg = data.history(context.asset, 'price', bar_count=50, frequency="1d").mean() - long_mavg = data.history(context.asset, 'price', bar_count=150, frequency="1d").mean() + # Compute moving averages calling data.history() for each + # moving average with the appropriate parameters. We choose to use + # minute bars for this simulation -> freq="1m" + # Returns a pandas dataframe. + short_mavg = data.history(context.asset, 'price', + bar_count=short_window, frequency="1m").mean() + long_mavg = data.history(context.asset, 'price', + bar_count=long_window, frequency="1m").mean() - # Trading logic - if short_mavg > long_mavg: - # order_target orders as many shares as needed to - # achieve the desired number of shares. - order_target(context.asset, 100) - elif short_mavg < long_mavg: - order_target(context.asset, 0) + # Let's keep the price of our asset in a more handy variable + price = data.current(context.asset, 'price') + + # If base_price is not set, we use the current value. This is the + # price at the first bar which we reference to calculate price_change. + if context.base_price is None: + context.base_price = price + price_change = (price - context.base_price) / context.base_price + + # Save values for later inspection + record(price=price, + cash=context.portfolio.cash, + price_change=price_change, + short_mavg=short_mavg, + long_mavg=long_mavg) + + # Since we are using limit orders, some orders may not execute immediately + # we wait until all orders are executed before considering more trades. + orders = get_open_orders(context.asset) + if len(orders) > 0: + return + + # Exit if we cannot trade + if not data.can_trade(context.asset): + return + + # We check what's our position on our portfolio and trade accordingly + pos_amount = context.portfolio.positions[context.asset].amount + + # Trading logic + if short_mavg > long_mavg and pos_amount == 0: + # we buy 100% of our portfolio for this asset + order_target_percent(context.asset, 1) + elif short_mavg < long_mavg and pos_amount > 0: + # we sell all our positions for this asset + order_target_percent(context.asset, 0) - # Save values for later inspection - record(btc=data.current(context.asset, 'price'), - short_mavg=short_mavg, - long_mavg=long_mavg) def analyze(context, perf): - import matplotlib.pyplot as plt - fig = plt.figure(figsize=(12,12)) - ax1 = fig.add_subplot(211) - perf.portfolio_value.plot(ax=ax1) - ax1.set_ylabel('portfolio value in $') - ax2 = fig.add_subplot(212) - perf['btc'].plot(ax=ax2) - perf[['short_mavg', 'long_mavg']].plot(ax=ax2) + # Get the base_currency that was passed as a parameter to the simulation + base_currency = context.exchanges.values()[0].base_currency.upper() - perf_trans = perf.ix[[t != [] for t in perf.transactions]] - buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]] - sells = perf_trans.ix[ - [t[0]['amount'] < 0 for t in perf_trans.transactions]] - ax2.plot(buys.index, perf.short_mavg.ix[buys.index], - '^', markersize=10, color='m') - ax2.plot(sells.index, perf.short_mavg.ix[sells.index], - 'v', markersize=10, color='k') - ax2.set_ylabel('price in $') - plt.legend(loc=0) - plt.show() + # First chart: Plot portfolio value using base_currency + ax1 = plt.subplot(411) + perf.loc[:, ['portfolio_value']].plot(ax=ax1) + ax1.legend_.remove() + ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency)) + start, end = ax1.get_ylim() + ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5)) -Here we are explicitly defining an ``analyze()`` function that gets -automatically called once the backtest is done. + # Second chart: Plot asset price, moving averages and buys/sells + ax2 = plt.subplot(412, sharex=ax1) + perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price') + ax2.legend_.remove() + ax2.set_ylabel('{asset}\n({base})'.format( + asset = context.asset.symbol, + base = base_currency + )) + start, end = ax2.get_ylim() + ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5)) + + transaction_df = extract_transactions(perf) + if not transaction_df.empty: + buy_df = transaction_df[transaction_df['amount'] > 0] + sell_df = transaction_df[transaction_df['amount'] < 0] + ax2.scatter( + buy_df.index.to_pydatetime(), + perf.loc[buy_df.index, 'price'], + marker='^', + s=100, + c='green', + label='' + ) + ax2.scatter( + sell_df.index.to_pydatetime(), + perf.loc[sell_df.index, 'price'], + marker='v', + s=100, + c='red', + label='' + ) + + # Third chart: Compare percentage change between our portfolio + # and the price of the asset + ax3 = plt.subplot(413, sharex=ax1) + perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3) + ax3.legend_.remove() + ax3.set_ylabel('Percent Change') + start, end = ax3.get_ylim() + ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5)) + + # Fourth chart: Plot our cash + ax4 = plt.subplot(414, sharex=ax1) + perf.cash.plot(ax=ax4) + ax4.set_ylabel('Cash\n({})'.format(base_currency)) + start, end = ax4.get_ylim() + ax4.yaxis.set_ticks(np.arange(0, end, end/5)) + + plt.show() + + + if __name__ == '__main__': + run_algorithm( + capital_base=1000, + data_frequency='minute', + initialize=initialize, + handle_data=handle_data, + analyze=analyze, + exchange_name='bitfinex', + algo_namespace=NAMESPACE, + base_currency='usd', + start=pd.to_datetime('2017-9-22', utc=True), + end=pd.to_datetime('2017-9-23', utc=True), + ) + +In order to run the code above, you have to ingest the needed data first: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -f minute -i ltc_usd + +And then run the code above with the following command: + +.. code-block:: bash + + catalyst run -f dual_moving_average.py -x bitfinex -s 2017-9-22 -e 2017-9-23 --capital-base 1000 --base-currency usd --data-frequency minute -o out.pickle + +Alternatively, we can make use of the ``run_algorithm()`` function included at +the end of the file, where we can specify all the simulation parameters, and +execute this file as a Python script: + +.. code-block:: bash + + python dual_moving_average.py + +Either way, we obtain the following charts: + +.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png + + +A few comments on the code above: + + At the beginning of our code, we import a number of Python libraries that we + will be using in different parts of our script. It's good practice to keep all + imports at the beginning of the file, as they are available globally + throughout our script. All the libraries imported in this example are already + present in your environment since they are prerequisites for the Catalyst + installation. + + Focus on the code that is inside ``handle_data()`` that is where all the + trading logic occurs. You can safely dismiss most of the code in the + ``analyze()`` section, which is mostly to customize the visualization of the + performance of our algorithm using the matplotlib library. You can copy and + paste this whole section into other algorithms to obtain a similar display. + + Inside the ``handle_data()``, we also used the ``order_target_percent()`` + function above. This and other functions like it can make order management + and portfolio rebalancing much easier. + + The ``ltc_usd`` asset was arbitrarily chosen. The values of 50 and 200 for the + ``short_window`` and ``long_window`` parameters are fairly common for a dual + moving average crossover strategy from the world of traditional stocks (but + bear in mind that they are usually used with daily bars instead of minute + bars). The ``start`` and ``end`` dates have been chosen so as to demonstrate + how our strategy can both perform better (blue line above green line on the + ``Percent Change`` chart) and worse (green line above blue line towards the end) than the + price of the asset we are trading. + + You can change any of these parameters: ``asset``, ``short_window``, + ``long_window``, ``start_date`` and ``end_date`` and compare the results, and + you will see that in most cases, the performance is either worse than the + price of the asset, or you are overfitting to one specific case. As we said + at the beginning of this section, this strategy is probably not used by any + serious trader anymore, but its educational purpose. Although it might not be directly apparent, the power of ``history()`` (pun intended) can not be under-estimated as most algorithms make use of @@ -643,21 +804,13 @@ the ``scikit-learn`` functions require ``numpy.ndarray``\ s rather than ``pandas.DataFrame``\ s, so you can simply pass the underlying ``ndarray`` of a ``DataFrame`` via ``.values``). -We also used the ``order_target()`` function above. This and other -functions like it can make order management and portfolio rebalancing -much easier. - -Conclusions -~~~~~~~~~~~ +Next steps +~~~~~~~~~~ We hope that this tutorial gave you a little insight into the -architecture, API, and features of ``catalyst``. For next steps, check -out some of the -`examples `__. -The natural next step would be too look into the -`buy_and_hodl `_ -example, which is a more elaborated and realistic version of the ``buy_btc_simple`` example presented in this tutorial. +architecture, API, and features of Catalyst. For next steps, check +out some of the other :doc:`example algorithms`. Feel free to ask questions on the ``#catalyst_dev`` channel of our `Discord group `__ and report diff --git a/appendix.html b/appendix.html index 3cd6d608..80bd239b 100644 --- a/appendix.html +++ b/appendix.html @@ -103,7 +103,7 @@
  • Working example: Dual Moving Average Cross-Over
    • -
  • Conclusions
    • +
  • Next steps
  • Catalyst & Jupyter Notebook
    • -
  • Conclusions
    • +
  • Next steps
  • Catalyst & Jupyter Notebook
      @@ -150,46 +150,41 @@
  • Release Notes
      -
    • Version 0.3.9 -
    • Version 0.3.8
    • Version 0.3.7
    • Version 0.3.6
    • Version 0.3.5
    • Version 0.3.4
    • Version 0.3.3 +
      • +
    • Version 0.3.2
      • -
    • Version 0.3.2 -
    • Version 0.3.1
    • Version 0.3
      • @@ -371,13 +366,16 @@ following from the command line:

      catalyst provides three interfaces:

      • A command-line interface (CLI),
        • -
      • the IPython Notebook magic,
        • -
      • and a run_algorithm() that you can call from other -Python scripts.
        • +
      • a run_algorithm() that you can call from other +Python scripts,
        • +
      • and the Jupyter Notebook magic.
      -

      We’ll start with the CLI, and introduce the IPython Notebook below. Some of -the example algorithms provide instructions on how to run -them both from the CLI, and using the run_algorithm() function.

      +

      We’ll start with the CLI, and introduce the run_algorithm() in the last +example of this tutorial. Some of the example algorithms +provide instructions on how to run them both from the CLI, and using the +run_algorithm() function. For the third method, refer to the +corresponding section on Catalyst & Jupyter Notebook after you +have assimilated the contents of this tutorial.

      Command line interfaceΒΆ

      After you installed Catalyst, you should be able to execute the following @@ -462,7 +460,7 @@ conveniently pass to the -c

      Thus, to execute our algorithm from above and save the results to buy_btc_simple_out.pickle we would call catalyst run as follows:

      -
      catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -c usd --capital-base 100000 -o buy_btc_simple_out.pickle
+
      catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -c usd --capital-base 100000 -o buy_btc_simple_out.pickle
 
      
 
      
 
      INFO: run_algo: running algo in backtest mode
@@ -731,74 +729,220 @@ we assume that the stock price has upwards momentum and long the stock.
 If the short-mavg crosses from above we exit the positions as we assume
 the stock to go down further.
      
 
      As we need to have access to previous prices to implement this strategy
-we need a new concept: History
      
-
      data.history() is a convenience function that keeps a rolling window of
-data for you. The first argument is the number of bars you want to
-collect, the second argument is the unit (either '1d' for '1m'
-but note that you need to have minute-level data for using 1m). This is
-a function we use in the handle_data() section:
      
-
      %load_ext catalyst
-
      
-
      
-
      %%catalyst --start 2016-4-1 --end 2017-9-30 -x bitfinex
+we need a new concept: History. data.history() is a convenience function
+that keeps a rolling window of data for you. The first argument is the number
+of bars you want to collect, the second argument is the unit (either '1d'
+for daily or '1m' for minute frequency, but note that you need to have
+minute-level data when using 1m). This is a function we use in the
+handle_data() section.
      
+
      You will note that the code below is substantially longer than the previous
+examples. Don’t get overwhelmed by it as the logic is fairly simple and easy to
+follow. Most of the added some complexity has been added to beautify the output,
+which you can skim through for now. A copy of this algorithm is available in
+the examples directory:
+dual_moving_average.py.
      
+
      import numpy as np
+import pandas as pd
+from logbook import Logger
+import matplotlib.pyplot as plt
 
-from catalyst.api import order, record, symbol, order_target
+from catalyst import run_algorithm
+from catalyst.api import (order, record, symbol, order_target_percent,
+        get_open_orders)
+from catalyst.exchange.stats_utils import extract_transactions
+
+NAMESPACE = 'dual_moving_average'
+log = Logger(NAMESPACE)
 
 def initialize(context):
-   context.i = 0
-   context.asset = symbol('btc_usd')
+    context.i = 0
+    context.asset = symbol('ltc_usd')
+    context.base_price = None
+
 
 def handle_data(context, data):
-   # Skip first 150 days to get full windows
-   context.i += 1
-   if context.i < 150:
+    # define the windows for the moving averages
+    short_window = 50
+    long_window = 200
+
+    # Skip as many bars as long_window to properly compute the average
+    context.i += 1
+    if context.i < long_window:
        return
 
-   # Compute averages
-   # data.history() has to be called with the same params
-   # from above and returns a pandas dataframe.
-   short_mavg = data.history(context.asset, 'price', bar_count=50, frequency="1d").mean()
-   long_mavg = data.history(context.asset, 'price', bar_count=150, frequency="1d").mean()
+    # Compute moving averages calling data.history() for each
+    # moving average with the appropriate parameters. We choose to use
+    # minute bars for this simulation -> freq="1m"
+    # Returns a pandas dataframe.
+    short_mavg = data.history(context.asset, 'price',
+                        bar_count=short_window, frequency="1m").mean()
+    long_mavg = data.history(context.asset, 'price',
+                        bar_count=long_window, frequency="1m").mean()
 
-   # Trading logic
-   if short_mavg > long_mavg:
-       # order_target orders as many shares as needed to
-       # achieve the desired number of shares.
-       order_target(context.asset, 100)
-   elif short_mavg < long_mavg:
-       order_target(context.asset, 0)
+    # Let's keep the price of our asset in a more handy variable
+    price = data.current(context.asset, 'price')
+
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
+    if context.base_price is None:
+        context.base_price = price
+    price_change = (price - context.base_price) / context.base_price
+
+    # Save values for later inspection
+    record(price=price,
+           cash=context.portfolio.cash,
+           price_change=price_change,
+           short_mavg=short_mavg,
+           long_mavg=long_mavg)
+
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
+    orders = get_open_orders(context.asset)
+    if len(orders) > 0:
+        return
+
+    # Exit if we cannot trade
+    if not data.can_trade(context.asset):
+        return
+
+    # We check what's our position on our portfolio and trade accordingly
+    pos_amount = context.portfolio.positions[context.asset].amount
+
+    # Trading logic
+    if short_mavg > long_mavg and pos_amount == 0:
+       # we buy 100% of our portfolio for this asset
+       order_target_percent(context.asset, 1)
+    elif short_mavg < long_mavg and pos_amount > 0:
+       # we sell all our positions for this asset
+       order_target_percent(context.asset, 0)
 
-   # Save values for later inspection
-   record(btc=data.current(context.asset, 'price'),
-          short_mavg=short_mavg,
-          long_mavg=long_mavg)
 
 def analyze(context, perf):
-   import matplotlib.pyplot as plt
-   fig = plt.figure(figsize=(12,12))
-   ax1 = fig.add_subplot(211)
-   perf.portfolio_value.plot(ax=ax1)
-   ax1.set_ylabel('portfolio value in $')
 
-   ax2 = fig.add_subplot(212)
-   perf['btc'].plot(ax=ax2)
-   perf[['short_mavg', 'long_mavg']].plot(ax=ax2)
+    # Get the base_currency that was passed as a parameter to the simulation
+    base_currency = context.exchanges.values()[0].base_currency.upper()
 
-   perf_trans = perf.ix[[t != [] for t in perf.transactions]]
-   buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]
-   sells = perf_trans.ix[
-       [t[0]['amount'] < 0 for t in perf_trans.transactions]]
-   ax2.plot(buys.index, perf.short_mavg.ix[buys.index],
-            '^', markersize=10, color='m')
-   ax2.plot(sells.index, perf.short_mavg.ix[sells.index],
-            'v', markersize=10, color='k')
-   ax2.set_ylabel('price in $')
-   plt.legend(loc=0)
-   plt.show()
+    # First chart: Plot portfolio value using base_currency
+    ax1 = plt.subplot(411)
+    perf.loc[:, ['portfolio_value']].plot(ax=ax1)
+    ax1.legend_.remove()
+    ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
+    start, end = ax1.get_ylim()
+    ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+
+    # Second chart: Plot asset price, moving averages and buys/sells
+    ax2 = plt.subplot(412, sharex=ax1)
+    perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
+    ax2.legend_.remove()
+    ax2.set_ylabel('{asset}\n({base})'.format(
+        asset = context.asset.symbol,
+        base = base_currency
+        ))
+    start, end = ax2.get_ylim()
+    ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+
+    transaction_df = extract_transactions(perf)
+    if not transaction_df.empty:
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
+        ax2.scatter(
+            buy_df.index.to_pydatetime(),
+            perf.loc[buy_df.index, 'price'],
+            marker='^',
+            s=100,
+            c='green',
+            label=''
+        )
+        ax2.scatter(
+            sell_df.index.to_pydatetime(),
+            perf.loc[sell_df.index, 'price'],
+            marker='v',
+            s=100,
+            c='red',
+            label=''
+        )
+
+    # Third chart: Compare percentage change between our portfolio
+    # and the price of the asset
+    ax3 = plt.subplot(413, sharex=ax1)
+    perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
+    ax3.legend_.remove()
+    ax3.set_ylabel('Percent Change')
+    start, end = ax3.get_ylim()
+    ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
+
+    # Fourth chart: Plot our cash
+    ax4 = plt.subplot(414, sharex=ax1)
+    perf.cash.plot(ax=ax4)
+    ax4.set_ylabel('Cash\n({})'.format(base_currency))
+    start, end = ax4.get_ylim()
+    ax4.yaxis.set_ticks(np.arange(0, end, end/5))
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    run_algorithm(
+            capital_base=1000,
+            data_frequency='minute',
+            initialize=initialize,
+            handle_data=handle_data,
+            analyze=analyze,
+            exchange_name='bitfinex',
+            algo_namespace=NAMESPACE,
+            base_currency='usd',
+            start=pd.to_datetime('2017-9-22', utc=True),
+            end=pd.to_datetime('2017-9-23', utc=True),
+        )
 
      
 
      
-
      Here we are explicitly defining an analyze() function that gets
-automatically called once the backtest is done.
      
+
      In order to run the code above, you have to ingest the needed data first:
      
+
      catalyst ingest-exchange -x bitfinex -f minute -i ltc_usd
+
      
+
      
+
      And then run the code above with the following command:
      
+
      catalyst run -f dual_moving_average.py -x bitfinex -s 2017-9-22 -e 2017-9-23 --capital-base 1000 --base-currency usd --data-frequency minute -o out.pickle
+
      
+
      
+
      Alternatively, we can make use of the run_algorithm() function included at
+the end of the file, where we can specify all the simulation parameters, and
+execute this file as a Python script:
      
+
      python dual_moving_average.py
+
      
+
      
+
      Either way, we obtain the following charts:
      
+https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png
+
      A few comments on the code above:
      
+
      
+
      At the beginning of our code, we import a number of Python libraries that we
+will be using in different parts of our script. It’s good practice to keep all
+imports at the beginning of the file, as they are available globally
+throughout our script. All the libraries imported in this example are already
+present in your environment since they are prerequisites for the Catalyst
+installation.
      
+
      Focus on the code that is inside handle_data() that is where all the
+trading logic occurs. You can safely dismiss most of the code in the
+analyze() section, which is mostly to customize the visualization of the
+performance of our algorithm using the matplotlib library. You can copy and
+paste this whole section into other algorithms to obtain a similar display.
      
+
      Inside the handle_data(), we also used the order_target_percent()
+function above. This and other functions like it can make order management
+and portfolio rebalancing much easier.
      
+
      The ltc_usd asset was arbitrarily chosen. The values of 50 and 200 for the
+short_window and long_window parameters are fairly common for a dual
+moving average crossover strategy from the world of traditional stocks (but
+bear in mind that they are usually used with daily bars instead of minute
+bars). The start and end dates have been chosen so as to demonstrate
+how our strategy can both perform better (blue line above green line on the
+Percent Change chart) and worse (green line above blue line towards the end) than the
+price of the asset we are trading.
      
+
      You can change any of these parameters: asset, short_window,
+long_window, start_date and end_date and compare the results, and
+you will see that in most cases, the performance is either worse than the
+price of the asset, or you are overfitting to one specific case. As we said
+at the beginning of this section, this strategy is probably not used by any
+serious trader anymore, but its educational purpose.
      
+
      
 
      Although it might not be directly apparent, the power of history()
 (pun intended) can not be under-estimated as most algorithms make use of
 prior market developments in one form or another. You could easily
@@ -808,20 +952,13 @@ predict future market movements based on past prices (note, that most of
 the scikit-learn functions require numpy.ndarrays rather than
 pandas.DataFrames, so you can simply pass the underlying
 ndarray of a DataFrame via .values).
      
-
      We also used the order_target() function above. This and other
-functions like it can make order management and portfolio rebalancing
-much easier.
      
 
      
 
      
-
      
-
      ConclusionsΒΆ
      
+
      
+
      Next stepsΒΆ
      
 
      We hope that this tutorial gave you a little insight into the
-architecture, API, and features of catalyst. For next steps, check
-out some of the
-examples.
-The natural next step would be too look into the
-buy_and_hodl
-example, which is a more elaborated and realistic version of the buy_btc_simple example presented in this tutorial.
      
+architecture, API, and features of Catalyst. For next steps, check
+out some of the other example algorithms.
      
 
      Feel free to ask questions on the #catalyst_dev channel of our
 Discord group and report
 problems on our GitHub issue tracker.
      
diff --git a/bundles.html b/bundles.html
index bb0d64da..5a013830 100644
--- a/bundles.html
+++ b/bundles.html
@@ -148,46 +148,41 @@
  • Release Notes
  • Release Notes
  • Release Notes
    • -
  • Conclusions
    • +
  • Next steps
  • Catalyst & Jupyter Notebook
  • Catalyst & Jupyter Notebook
      diff --git a/install.html b/install.html index 8e149d20..b593df8d 100644 --- a/install.html +++ b/install.html @@ -150,46 +150,41 @@
  • Release Notes
  • Release Notes
  • Release Notes
  • Release Notes
  • Release Notes
    • -
  • Conclusions
    • +
  • Next steps
  • Catalyst & Jupyter Notebook
      diff --git a/resources.html b/resources.html index 6d84e03d..7bf16674 100644 --- a/resources.html +++ b/resources.html @@ -150,46 +150,41 @@
  • Release Notes
    • -
  • Conclusions
    • +
  • Next steps
  • Catalyst & Jupyter Notebook
      @@ -226,7 +226,7 @@
      • Docs »
        • -
      • +
      • Search
        • diff --git a/searchindex.js b/searchindex.js index 9b4b4577..c5ee822a 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({envversion:46,filenames:["appendix","beginner-tutorial","bundles","development-guidelines","example-algos","index","install","jupyter","live-trading","naming-convention","release-process","releases","resources","utilities","videos","welcome"],objects:{"zipline.data.bundles":{bundles:[0,0,1,""]}},objnames:{"0":["py","data","Python 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\ No newline at end of file diff --git a/utilities.html b/utilities.html index 8cd0dea4..dfdbcea7 100644 --- a/utilities.html +++ b/utilities.html @@ -150,46 +150,41 @@
    • Release Notes
    • Release Notes
    • Release Notes