BLD: minor adjustments to the talib sample algo

catalyst/examples/mean_reversion.py

@@ -19,7 +19,7 @@ from catalyst.api import symbol, record, order_target_percent, \
 # state using the files included in the folder.
 from catalyst.exchange.stats_utils import extract_transactions, trend_direction
 
-algo_namespace = 'momentum'
+algo_namespace = 'mean_reversion'
 log = Logger(algo_namespace)
 
 
@@ -30,7 +30,7 @@ def initialize(context):
     # parameters or values you're going to use.
 
     # In our example, we're looking at Ether in USD Tether.
-    context.eth_btc = symbol('etc_usdt')
+    context.eth_btc = symbol('neo_usd')
     context.base_price = None
     context.current_day = None
     context.trigger = None
@@ -256,18 +256,18 @@ if __name__ == '__main__':
     MODE = 'backtest'
 
     if MODE == 'backtest':
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py -x poloniex -s 2017-7-1 -e 2017-7-31 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
         run_algorithm(
             capital_base=1,
             data_frequency='minute',
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='poloniex',
+            exchange_name='bitfinex',
             algo_namespace=algo_namespace,
-            base_currency='usdt',
+            base_currency='usd',
-            start=pd.to_datetime('2017-7-1', utc=True),
+            start=pd.to_datetime('2017-10-1', utc=True),
-            # end=pd.to_datetime('2017-9-30', utc=True),
+            end=pd.to_datetime('2017-11-13', utc=True),
-            end=pd.to_datetime('2017-10-31', utc=True),
         )
 
     elif MODE == 'live':
@@ -275,9 +275,9 @@ if __name__ == '__main__':
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='poloniex',
+            exchange_name='bitfinex',
             live=True,
             algo_namespace=algo_namespace,
-            base_currency='usdt',
+            base_currency='usd',
             live_graph=True
         )

catalyst/examples/mean_reversion_simple.py

@@ -19,7 +19,7 @@ from catalyst.api import symbol, record, order_target_percent, \
 # state using the files included in the folder.
 from catalyst.exchange.stats_utils import extract_transactions, trend_direction
 
-algo_namespace = 'momentum'
+algo_namespace = 'mean_reversion_simple'
 log = Logger(algo_namespace)
 
 
@@ -30,7 +30,7 @@ def initialize(context):
     # parameters or values you're going to use.
 
     # In our example, we're looking at Ether in USD Tether.
-    context.eth_btc = symbol('etc_usdt')
+    context.eth_btc = symbol('neo_usd')
     context.base_price = None
     context.current_day = None
 
@@ -228,11 +228,11 @@ if __name__ == '__main__':
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='poloniex',
+            exchange_name='bitfinex',
             algo_namespace=algo_namespace,
-            base_currency='usdt',
+            base_currency='usd',
-            start=pd.to_datetime('2017-7-1', utc=True),
+            start=pd.to_datetime('2017-10-1', utc=True),
-            end=pd.to_datetime('2017-7-31', utc=True),
+            end=pd.to_datetime('2017-11-10', utc=True),
         )
 
     elif MODE == 'live':
@@ -240,9 +240,9 @@ if __name__ == '__main__':
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='poloniex',
+            exchange_name='bitfinex',
             live=True,
             algo_namespace=algo_namespace,
-            base_currency='usdt',
+            base_currency='usd',
             live_graph=True
         )

catalyst/examples/talib_simple.py

@@ -0,0 +1,364 @@
+# Run Command
+# catalyst run --start 2017-1-1 --end 2017-11-1 -o talib_simple.pickle -f talib_simple.py -x poloniex
+# 
+# Description
+# Simple TALib Example showing how to use various indicators in you strategy
+# Based loosly on https://github.com/mellertson/talib-macd-example/blob/master/talib-macd-matplotlib-example.py
+
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import talib as ta
+from logbook import Logger
+from matplotlib.dates import date2num
+from matplotlib.finance import candlestick_ohlc
+
+from catalyst import run_algorithm
+from catalyst.api import (
+    order,
+    order_target_percent,
+    symbol,
+)
+from catalyst.exchange.stats_utils import get_pretty_stats
+
+algo_namespace = 'talib_sample'
+log = Logger(algo_namespace)
+
+
+def initialize(context):
+    log.info('Starting TALib Simple Example')
+
+    context.ASSET_NAME = 'BTC_USDT'
+    context.asset = symbol(context.ASSET_NAME)
+
+    context.ORDER_SIZE = 10
+    context.SLIPPAGE_ALLOWED = 0.05
+
+    context.swallow_errors = True
+    context.errors = []
+
+    # Bars to look at per iteration should be bigger than SMA_SLOW
+    context.BARS = 365
+    context.COUNT = 0
+
+    # Technical Analysis Settings
+    context.SMA_FAST = 50
+    context.SMA_SLOW = 100
+    context.RSI_PERIOD = 14
+    context.RSI_OVER_BOUGHT = 80
+    context.RSI_OVER_SOLD = 20
+    context.RSI_AVG_PERIOD = 15
+    context.MACD_FAST = 12
+    context.MACD_SLOW = 26
+    context.MACD_SIGNAL = 9
+    context.STOCH_K = 14
+    context.STOCH_D = 3
+    context.STOCH_OVER_BOUGHT = 80
+    context.STOCH_OVER_SOLD = 20
+
+    pass
+
+
+def _handle_data(context, data):
+    # Get price, open, high, low, close
+    prices = data.history(
+        context.asset,
+        bar_count=context.BARS,
+        fields=['price', 'open', 'high', 'low', 'close'],
+        frequency='1d')
+
+    # Create a analysis data frame
+    analysis = pd.DataFrame(index=prices.index)
+
+    # SMA FAST
+    analysis['sma_f'] = ta.SMA(prices.close.as_matrix(), context.SMA_FAST)
+    # SMA SLOW
+    analysis['sma_s'] = ta.SMA(prices.close.as_matrix(), context.SMA_SLOW)
+
+    # Relative Strength Index
+    analysis['rsi'] = ta.RSI(prices.close.as_matrix(), context.RSI_PERIOD)
+    # RSI SMA
+    analysis['sma_r'] = ta.SMA(analysis.rsi.as_matrix(),
+                               context.RSI_AVG_PERIOD)
+
+    # MACD, MACD Signal, MACD Histogram
+    analysis['macd'], analysis['macdSignal'], analysis['macdHist'] = ta.MACD(
+        prices.close.as_matrix(), fastperiod=context.MACD_FAST,
+        slowperiod=context.MACD_SLOW, signalperiod=context.MACD_SIGNAL)
+
+    # Stochastics %K %D 
+    # %K = (Current Close - Lowest Low)/(Highest High - Lowest Low) * 100
+    # %D = 3-day SMA of %K
+    analysis['stoch_k'], analysis['stoch_d'] = ta.STOCH(
+        prices.high.as_matrix(), prices.low.as_matrix(),
+        prices.close.as_matrix(), slowk_period=context.STOCH_K,
+        slowd_period=context.STOCH_D)
+
+    # SMA FAST over SLOW Crossover
+    analysis['sma_test'] = np.where(analysis.sma_f > analysis.sma_s, 1, 0)
+
+    # MACD over Signal Crossover
+    analysis['macd_test'] = np.where((analysis.macd > analysis.macdSignal), 1,
+                                     0)
+
+    # Stochastics OVER BOUGHT & Decreasing
+    analysis['stoch_over_bought'] = np.where(
+        (analysis.stoch_k > context.STOCH_OVER_BOUGHT) & (
+            analysis.stoch_k > analysis.stoch_k.shift(1)), 1, 0)
+
+    # Stochastics OVER SOLD & Increasing
+    analysis['stoch_over_sold'] = np.where(
+        (analysis.stoch_k < context.STOCH_OVER_SOLD) & (
+            analysis.stoch_k > analysis.stoch_k.shift(1)), 1, 0)
+
+    # RSI OVER BOUGHT & Decreasing
+    analysis['rsi_over_bought'] = np.where(
+        (analysis.rsi > context.RSI_OVER_BOUGHT) & (
+            analysis.rsi < analysis.rsi.shift(1)), 1, 0)
+
+    # RSI OVER SOLD & Increasing
+    analysis['rsi_over_sold'] = np.where(
+        (analysis.rsi < context.RSI_OVER_SOLD) & (
+            analysis.rsi > analysis.rsi.shift(1)), 1, 0)
+
+    # Save the prices and analysis to send to analyze
+    context.prices = prices
+    context.analysis = analysis
+    context.price = data.current(context.asset, 'price')
+
+    makeOrders(context, analysis)
+
+    # Log the values of this bar
+    logAnalysis(analysis)
+
+
+def handle_data(context, data):
+    log.info('handling bar {}'.format(data.current_dt))
+    try:
+        _handle_data(context, data)
+    except Exception as e:
+        log.warn('aborting the bar on error {}'.format(e))
+        context.errors.append(e)
+
+    log.info('completed bar {}, total execution errors {}'.format(
+        data.current_dt,
+        len(context.errors)
+    ))
+
+    if len(context.errors) > 0:
+        log.info('the errors:\n{}'.format(context.errors))
+
+
+def analyze(context, results):
+    # Save results in CSV file
+    filename = os.path.splitext(os.path.basename('talib_simple'))[0]
+    results.to_csv(filename + '.csv')
+
+    log.info('the daily stats:\n{}'.format(get_pretty_stats(results)))
+    chart(context, context.prices, context.analysis, results)
+    pass
+
+
+def makeOrders(context, analysis):
+    if context.asset in context.portfolio.positions:
+
+        # Current position
+        position = context.portfolio.positions[context.asset]
+
+        if (position == 0):
+            log.info('Position Zero')
+            return
+
+        # Cost Basis
+        cost_basis = position.cost_basis
+
+        log.info(
+            'Holdings: {amount} @ {cost_basis}'.format(
+                amount=position.amount,
+                cost_basis=cost_basis
+            )
+        )
+
+        # Sell when holding and got sell singnal
+        if isSell(context, analysis):
+            profit = (context.price * position.amount) - (
+                cost_basis * position.amount)
+            order_target_percent(
+                asset=context.asset,
+                target=0,
+                limit_price=context.price * (1 - context.SLIPPAGE_ALLOWED),
+            )
+            log.info(
+                'Sold {amount} @ {price} Profit: {profit}'.format(
+                    amount=position.amount,
+                    price=context.price,
+                    profit=profit
+                )
+            )
+        else:
+            log.info('no buy or sell opportunity found')
+    else:
+        # Buy when not holding and got buy signal
+        if isBuy(context, analysis):
+            order(
+                asset=context.asset,
+                amount=context.ORDER_SIZE,
+                limit_price=context.price * (1 + context.SLIPPAGE_ALLOWED)
+            )
+            log.info(
+                'Bought {amount} @ {price}'.format(
+                    amount=context.ORDER_SIZE,
+                    price=context.price
+                )
+            )
+
+
+def isBuy(context, analysis):
+    # Bullish SMA Crossover
+    if (getLast(analysis, 'sma_test') == 1):
+        # Bullish MACD
+        if (getLast(analysis, 'macd_test') == 1):
+            return True
+
+    # # Bullish Stochastics
+    # if(getLast(analysis, 'stoch_over_sold') == 1):
+    #     return True
+
+    # # Bullish RSI
+    # if(getLast(analysis, 'rsi_over_sold') == 1):
+    #     return True
+
+    return False
+
+
+def isSell(context, analysis):
+    # Bearish SMA Crossover
+    if (getLast(analysis, 'sma_test') == 0):
+        # Bearish MACD
+        if (getLast(analysis, 'macd_test') == 0):
+            return True
+
+    # # Bearish Stochastics
+    # if(getLast(analysis, 'stoch_over_bought') == 0):
+    #     return True
+
+    # # Bearish RSI
+    # if(getLast(analysis, 'rsi_over_bought') == 0):
+    #     return True
+
+    return False
+
+
+def chart(context, prices, analysis, results):
+    results.portfolio_value.plot()
+
+    # Data for matplotlib finance plot
+    dates = date2num(prices.index.to_pydatetime())
+
+    # Create the Open High Low Close Tuple
+    prices_ohlc = [tuple([dates[i],
+                          prices.open[i],
+                          prices.high[i],
+                          prices.low[i],
+                          prices.close[i]]) for i in range(len(dates))]
+
+    fig = plt.figure(figsize=(14, 18))
+
+    # Draw the candle sticks
+    ax1 = fig.add_subplot(411)
+    ax1.set_ylabel(context.ASSET_NAME, size=20)
+    candlestick_ohlc(ax1, prices_ohlc, width=0.4, colorup='g', colordown='r')
+
+    # Draw Moving Averages
+    analysis.sma_f.plot(ax=ax1, c='r')
+    analysis.sma_s.plot(ax=ax1, c='g')
+
+    # RSI
+    ax2 = fig.add_subplot(412)
+    ax2.set_ylabel('RSI', size=12)
+    analysis.rsi.plot(ax=ax2, c='g',
+                      label='Period: ' + str(context.RSI_PERIOD))
+    analysis.sma_r.plot(ax=ax2, c='r',
+                        label='MA: ' + str(context.RSI_AVG_PERIOD))
+    ax2.axhline(y=30, c='b')
+    ax2.axhline(y=50, c='black')
+    ax2.axhline(y=70, c='b')
+    ax2.set_ylim([0, 100])
+    handles, labels = ax2.get_legend_handles_labels()
+    ax2.legend(handles, labels)
+
+    # Draw MACD computed with Talib
+    ax3 = fig.add_subplot(413)
+    ax3.set_ylabel('MACD: ' + str(context.MACD_FAST) + ', ' + str(
+        context.MACD_SLOW) + ', ' + str(context.MACD_SIGNAL), size=12)
+    analysis.macd.plot(ax=ax3, color='b', label='Macd')
+    analysis.macdSignal.plot(ax=ax3, color='g', label='Signal')
+    analysis.macdHist.plot(ax=ax3, color='r', label='Hist')
+    ax3.axhline(0, lw=2, color='0')
+    handles, labels = ax3.get_legend_handles_labels()
+    ax3.legend(handles, labels)
+
+    # Stochastic plot
+    ax4 = fig.add_subplot(414)
+    ax4.set_ylabel('Stoch (k,d)', size=12)
+    analysis.stoch_k.plot(ax=ax4, label='stoch_k:' + str(context.STOCH_K),
+                          color='r')
+    analysis.stoch_d.plot(ax=ax4, label='stoch_d:' + str(context.STOCH_D),
+                          color='g')
+    handles, labels = ax4.get_legend_handles_labels()
+    ax4.legend(handles, labels)
+    ax4.axhline(y=20, c='b')
+    ax4.axhline(y=50, c='black')
+    ax4.axhline(y=80, c='b')
+
+    plt.show()
+
+
+def logAnalysis(analysis):
+    # Log only the last value in the array
+    log.info('- sma_f:          {:.2f}'.format(getLast(analysis, 'sma_f')))
+    log.info('- sma_s:          {:.2f}'.format(getLast(analysis, 'sma_s')))
+
+    log.info('- rsi:            {:.2f}'.format(getLast(analysis, 'rsi')))
+    log.info('- sma_r:          {:.2f}'.format(getLast(analysis, 'sma_r')))
+
+    log.info('- macd:           {:.2f}'.format(getLast(analysis, 'macd')))
+    log.info(
+        '- macdSignal:     {:.2f}'.format(getLast(analysis, 'macdSignal')))
+    log.info('- macdHist:       {:.2f}'.format(getLast(analysis, 'macdHist')))
+
+    log.info('- stoch_k:        {:.2f}'.format(getLast(analysis, 'stoch_k')))
+    log.info('- stoch_d:        {:.2f}'.format(getLast(analysis, 'stoch_d')))
+
+    log.info('- sma_test:       {}'.format(getLast(analysis, 'sma_test')))
+    log.info('- macd_test:      {}'.format(getLast(analysis, 'macd_test')))
+
+    log.info('- stoch_over_bought:   {}'.format(
+        getLast(analysis, 'stoch_over_bought')))
+    log.info(
+        '- stoch_over_sold:   {}'.format(getLast(analysis, 'stoch_over_sold')))
+
+    log.info('- rsi_over_bought:       {}'.format(
+        getLast(analysis, 'rsi_over_bought')))
+    log.info(
+        '- rsi_over_sold:       {}'.format(getLast(analysis, 'rsi_over_sold')))
+
+
+def getLast(arr, name):
+    return arr[name][arr[name].index[-1]]
+
+
+if __name__ == '__main__':
+    run_algorithm(
+        capital_base=10000,
+        data_frequency='daily',
+        initialize=initialize,
+        handle_data=handle_data,
+        analyze=analyze,
+        exchange_name='poloniex',
+        base_currency='usdt',
+        start=pd.to_datetime('2016-11-1', utc=True),
+        end=pd.to_datetime('2017-11-10', utc=True),
+    )

docs/source/install.rst

@@ -136,14 +136,17 @@ about matplotlib backends, please refer to the
 Windows
 ~~~~~~~
 
-In Windows, you will need the `Microsoft Visual C++ Compiler for Python 2.7 
+In Windows, you will first need to install the `Microsoft Visual C++ Compiler 
+for Python 2.7 
 <https://www.microsoft.com/en-us/download/details.aspx?id=44266>`_. This 
 package contains the compiler and the set of system headers necessary for 
 producing binary wheels for Python 2.7 packages. If it's not already in your 
 system, download it and install it before proceeding to the next step.
 
-For windows, the easiest and best supported way to install Catalyst is to use
+Once you have the above compiler installed, the easiest and best supported way 
-:ref:`Conda <conda>`.
+to install Catalyst in Windows is to use :ref:`Conda <conda>`. If you didn't 
+any problems installing the compiler, jump to the :ref:`Conda <conda>` section, 
+otherwise keep on reading to troubleshoot the C++ compiler installtion.
 
 Some problems we have encountered installing the **Visual C++ Compiler** 
 mentioned above are as follows:
@@ -158,6 +161,8 @@ mentioned above are as follows:
     ``Registry Editor``
   - Navigate to the following folder:
     ``HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\Installer``
+  - If the last folder does not exist, create it by right-clicking on the 
+    parent folder and choosing -> ``New`` -> ``Key`` and typing ``Installer``
   - If there is an entry for ``DisableMSI``, set the Value data to 0.
   - If there is no such entry, click on the ``Edit`` menu -> ``New`` -> 
     ``DWORD (32-bit) Value`` and enter ``DisableMSI`` as the Name (and by 
@@ -302,9 +307,9 @@ understands the complex binary dependencies of packages like ``numpy`` and
 dependencies without requiring the use of a second tool to acquire Catalyst's 
 non-Python dependencies.
 
-  For Windows, you will need the *Microsoft Visual C++ Compiler for Python 
+  For Windows, you will first need to install the *Microsoft Visual C++ 
-  2.7*. Follow the instructions on the :ref:`Windows` section and come back 
+  Compiler for Python 2.7*. Follow the instructions on the :ref:`Windows` 
-  here.
+  section and come back here.
 
 For instructions on how to install ``conda``, see the `Conda Installation
 Documentation <http://conda.pydata.org/docs/download.html>`_. Alternatively, 
@@ -319,10 +324,23 @@ main packages needed. To install MiniConda, you can follow these steps:
 3. Ensure the correct installation by running ``conda list`` in a Terminal 
    window, which should print the list of packages installed with Conda.
 
+  For Windows, if you accepted the default installation options, you didn't 
+  check an option to add Conda to the PATH, so trying to run ``conda`` from
+  a regular ``Command Prompt`` will result in the following error: ``'conda' 
+  is no recognized as an internal or external command, operatble program or 
+  batch file``. That's to be expected. You will nee to launch an ``Anaconda 
+  Prompt`` that was added at installation time to your list of programs 
+  available from the Start menu. 
+
 Once either Conda or MiniConda has been set up you can install Catalyst:
 
 1. Download the file `python2.7-environment.yml 
    <https://github.com/enigmampc/catalyst/blob/master/etc/python2.7-environment.yml>`_.
+
+     To download, simply click on the 'Raw' button and save the file locally to 
+     a folder you can remember. Make sure that the file gets saved with the ``.yml`` 
+     extension, and nothing like a ``.txt`` file or anything else.
+
 2. Open a Terminal window and enter [``cd/dir``] into the directory where you 
    saved the above ``python2.7-environment.yml`` file.
 3. Install using this file. This step can take about 5-10 minutes to install.
@@ -346,6 +364,14 @@ Once either Conda or MiniConda has been set up you can install Catalyst:
 
       activate catalyst
 
+5. Verify that Catalyst is install correctly:
+
+   .. code-block:: bash
+
+     catalyst --version
+
+   which should display the current version.
+
 Congratulations! You now have Catalyst installed.
 
 Troubleshooting ``conda`` Install
@@ -355,13 +381,21 @@ If the command  ``conda env create -f python2.7-environment.yml`` in step 3
 above failed for any reason, you can try setting up the environment manually 
 with the following steps:
 
-1. Create the environment:
+1. If the above installation failed, and you have a partially set up catalyst
+   environment, remove it first. If you are starting from scratch, proceed to 
+   step #2:
+
+   .. code-block:: bash
+
+      conda env remove --name catalyst
+
+2. Create the environment:
 
    .. code-block:: bash
 
       conda create --name catalyst python=2.7 scipy zlib
 
-2. Activate the environment:
+3. Activate the environment:
 
    **Linux or OSX:**
 
@@ -375,12 +409,22 @@ with the following steps:
 
       activate catalyst
 
-3. Install the Catalyst inside the environment:
+4. Install the Catalyst inside the environment:
 
    .. code-block:: bash
 
       pip install enigma-catalyst matplotlib
 
+5. Verify that Catalyst is installed correctly:
+
+   .. code-block:: bash
+
+     catalyst --version
+
+   which should display the current version.
+
+Congratulations! You now have Catalyst properly installed.
+
 Getting Help
 ------------