mirror of
https://github.com/wassname/catalyst.git
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Merge remote-tracking branch 'origin/develop' into develop
# Conflicts: # catalyst/examples/mean_reversion_simple.py
This commit is contained in:
fredfortier
@@ -0,0 +1,3 @@
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An overview of most of the trading strategies in this folder can be found in the
|
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`Examples Algorithms <https://enigmampc.github.io/catalyst/example-algos.html>`_
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section of our documentation website.
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@@ -15,15 +15,11 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pandas as pd
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import matplotlib.pyplot as plt
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from catalyst import run_algorithm
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from catalyst.api import (
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order_target_value,
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symbol,
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record,
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cancel_order,
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get_open_orders,
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)
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from catalyst.api import (order_target_value, symbol, record,
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cancel_order, get_open_orders, )
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def initialize(context):
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@@ -78,15 +74,14 @@ def handle_data(context, data):
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def analyze(context=None, results=None):
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import matplotlib.pyplot as plt
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# Plot the portfolio and asset data.
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ax1 = plt.subplot(611)
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results[['portfolio_value']].plot(ax=ax1)
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ax1.set_ylabel('Portfolio Value (USD)')
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ax1.set_ylabel('Portfolio\nValue\n(USD)')
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ax2 = plt.subplot(612, sharex=ax1)
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ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
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ax2.set_ylabel('{asset}\n(USD)'.format(asset=context.ASSET_NAME))
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results[['price']].plot(ax=ax2)
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trans = results.ix[[t != [] for t in results.transactions]]
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@@ -126,11 +121,11 @@ def analyze(context=None, results=None):
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'algorithm',
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'benchmark',
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]].plot(ax=ax5)
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ax5.set_ylabel('Percent Change')
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ax5.set_ylabel('Percent\nChange')
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ax6 = plt.subplot(616, sharex=ax1)
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results[['volume']].plot(ax=ax6)
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ax6.set_ylabel('Volume (mCoins/5min)')
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ax6.set_ylabel('Volume')
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plt.legend(loc=3)
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@@ -142,13 +137,13 @@ def analyze(context=None, results=None):
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if __name__ == '__main__':
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run_algorithm(
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capital_base=10000,
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data_frequency='minute',
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data_frequency='daily',
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initialize=initialize,
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handle_data=handle_data,
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analyze=analyze,
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exchange_name='bitfinex',
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algo_namespace='buy_and_hodl',
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base_currency='usd',
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start=pd.to_datetime('2017-11-01', utc=True),
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end=pd.to_datetime('2017-11-10', utc=True),
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start=pd.to_datetime('2015-03-01', utc=True),
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end=pd.to_datetime('2017-10-31', utc=True),
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)
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@@ -3,7 +3,8 @@
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https://enigmampc.github.io/catalyst/beginner-tutorial.html
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Run this example, by executing the following from your terminal:
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catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
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catalyst ingest-exchange -x bitfinex -f daily -i btc_usdt
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catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
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If you want to run this code using another exchange, make sure that
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the asset is available on that exchange. For example, if you were to run
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@@ -12,7 +13,7 @@
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context.asset = symbol('btc_usdt') # note 'usdt' instead of 'usd'
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and specify exchange poloniex as follows:
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catalyst ingest-exchange -x poloniex -f daily -i btc_usdt
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catalyst run -f buy_btc_simple.py -x poloniex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
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To see which assets are available on each exchange, visit:
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|
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@@ -0,0 +1,153 @@
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import numpy as np
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import pandas as pd
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from logbook import Logger
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import matplotlib.pyplot as plt
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from catalyst import run_algorithm
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from catalyst.api import (order, record, symbol, order_target_percent,
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get_open_orders)
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from catalyst.exchange.stats_utils import extract_transactions
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NAMESPACE = 'dual_moving_average'
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log = Logger(NAMESPACE)
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def initialize(context):
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context.i = 0
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context.asset = symbol('ltc_usd')
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context.base_price = None
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def handle_data(context, data):
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# define the windows for the moving averages
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short_window = 50
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long_window = 200
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# Skip as many bars as long_window to properly compute the average
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context.i += 1
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if context.i < long_window:
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return
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# Compute moving averages calling data.history() for each
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# moving average with the appropriate parameters. We choose to use
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# minute bars for this simulation -> freq="1m"
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# Returns a pandas dataframe.
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short_mavg = data.history(context.asset, 'price',
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bar_count=short_window, frequency="1m").mean()
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long_mavg = data.history(context.asset, 'price',
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bar_count=long_window, frequency="1m").mean()
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# Let's keep the price of our asset in a more handy variable
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price = data.current(context.asset, 'price')
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# If base_price is not set, we use the current value. This is the
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# price at the first bar which we reference to calculate price_change.
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if context.base_price is None:
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context.base_price = price
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price_change = (price - context.base_price) / context.base_price
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# Save values for later inspection
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record(price=price,
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cash=context.portfolio.cash,
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price_change=price_change,
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short_mavg=short_mavg,
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long_mavg=long_mavg)
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# Since we are using limit orders, some orders may not execute immediately
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# we wait until all orders are executed before considering more trades.
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orders = get_open_orders(context.asset)
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if len(orders) > 0:
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return
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# Exit if we cannot trade
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if not data.can_trade(context.asset):
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return
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# We check what's our position on our portfolio and trade accordingly
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pos_amount = context.portfolio.positions[context.asset].amount
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# Trading logic
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if short_mavg > long_mavg and pos_amount == 0:
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# we buy 100% of our portfolio for this asset
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order_target_percent(context.asset, 1)
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elif short_mavg < long_mavg and pos_amount > 0:
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# we sell all our positions for this asset
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order_target_percent(context.asset, 0)
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def analyze(context, perf):
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# Get the base_currency that was passed as a parameter to the simulation
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base_currency = context.exchanges.values()[0].base_currency.upper()
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# First chart: Plot portfolio value using base_currency
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ax1 = plt.subplot(411)
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perf.loc[:, ['portfolio_value']].plot(ax=ax1)
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ax1.legend_.remove()
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ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
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start, end = ax1.get_ylim()
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ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
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# Second chart: Plot asset price, moving averages and buys/sells
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ax2 = plt.subplot(412, sharex=ax1)
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perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
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ax2.legend_.remove()
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ax2.set_ylabel('{asset}\n({base})'.format(
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asset = context.asset.symbol,
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base = base_currency
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))
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start, end = ax2.get_ylim()
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ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
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transaction_df = extract_transactions(perf)
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if not transaction_df.empty:
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buy_df = transaction_df[transaction_df['amount'] > 0]
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sell_df = transaction_df[transaction_df['amount'] < 0]
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ax2.scatter(
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buy_df.index.to_pydatetime(),
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perf.loc[buy_df.index, 'price'],
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marker='^',
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s=100,
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c='green',
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label=''
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)
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ax2.scatter(
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sell_df.index.to_pydatetime(),
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perf.loc[sell_df.index, 'price'],
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marker='v',
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s=100,
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c='red',
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label=''
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)
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# Third chart: Compare percentage change between our portfolio
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# and the price of the asset
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ax3 = plt.subplot(413, sharex=ax1)
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perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
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ax3.legend_.remove()
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ax3.set_ylabel('Percent Change')
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start, end = ax3.get_ylim()
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ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
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# Fourth chart: Plot our cash
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ax4 = plt.subplot(414, sharex=ax1)
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perf.cash.plot(ax=ax4)
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ax4.set_ylabel('Cash\n({})'.format(base_currency))
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start, end = ax4.get_ylim()
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ax4.yaxis.set_ticks(np.arange(0, end, end/5))
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plt.show()
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if __name__ == '__main__':
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run_algorithm(
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capital_base=1000,
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data_frequency='minute',
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initialize=initialize,
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handle_data=handle_data,
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analyze=analyze,
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exchange_name='bitfinex',
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algo_namespace=NAMESPACE,
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base_currency='usd',
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start=pd.to_datetime('2017-9-22', utc=True),
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end=pd.to_datetime('2017-9-23', utc=True),
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)
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@@ -5,6 +5,7 @@ import os
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import tempfile
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import time
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import numpy as np
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import pandas as pd
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import talib
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from logbook import Logger
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@@ -31,7 +32,7 @@ def initialize(context):
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# trading pairs) you want to backtest. You'll also want to define any
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# parameters or values you're going to use.
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# In our example, we're looking at Ether in USD Tether.
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# In our example, we're looking at Neo in Ether.
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context.neo_eth = symbol('neo_eth')
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context.base_price = None
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context.current_day = None
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@@ -160,13 +161,13 @@ def analyze(context=None, perf=None):
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# Plot the portfolio value over time.
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ax1 = plt.subplot(611)
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perf.loc[:, 'portfolio_value'].plot(ax=ax1)
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ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
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ax1.set_ylabel('Portfolio\nValue\n({})'.format(base_currency))
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# Plot the price increase or decrease over time.
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ax2 = plt.subplot(612, sharex=ax1)
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perf.loc[:, 'price'].plot(ax=ax2, label='Price')
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ax2.set_ylabel('{asset} ({base})'.format(
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ax2.set_ylabel('{asset}\n({base})'.format(
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asset=context.neo_eth.symbol, base=base_currency
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))
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@@ -195,18 +196,19 @@ def analyze(context=None, perf=None):
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perf.loc[:, 'cash'].plot(
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ax=ax4, label='Base Currency ({})'.format(base_currency)
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)
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ax4.set_ylabel('Cash ({})'.format(base_currency))
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ax4.set_ylabel('Cash\n({})'.format(base_currency))
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perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
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ax5 = plt.subplot(614, sharex=ax1)
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perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
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ax5.set_ylabel('Percent Change')
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ax5.set_ylabel('Percent\nChange')
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ax6 = plt.subplot(615, sharex=ax1)
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perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
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ax6.axhline(70, color='darkgoldenrod')
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ax6.axhline(30, color='darkgoldenrod')
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ax6.set_ylabel('RSI')
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ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
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ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
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if not transaction_df.empty:
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ax6.scatter(
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@@ -226,6 +228,8 @@ def analyze(context=None, perf=None):
|
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label=''
|
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)
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plt.legend(loc=3)
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start, end = ax6.get_ylim()
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ax6.yaxis.set_ticks(np.arange(0, end, end/5))
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# Show the plot.
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plt.gcf().set_size_inches(18, 8)
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@@ -245,7 +249,7 @@ if __name__ == '__main__':
|
||||
|
||||
timestr = time.strftime('%Y%m%d-%H%M%S')
|
||||
out = os.path.join(folder, '{}.p'.format(timestr))
|
||||
# catalyst run -f catalyst/examples/mean_reversion_simple.py -x poloniex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
|
||||
# catalyst run -f catalyst/examples/mean_reversion_simple.py -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
|
||||
run_algorithm(
|
||||
capital_base=0.5,
|
||||
data_frequency='minute',
|
||||
|
||||
@@ -0,0 +1,133 @@
|
||||
'''Use this code to execute a portfolio optimization model. This code
|
||||
will select the portfolio with the maximum Sharpe Ratio. The parameters
|
||||
are set to use 180 days of historical data and rebalance every 30 days.
|
||||
|
||||
This is the code used in the following article:
|
||||
https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556
|
||||
|
||||
You can run this code using the Python interpreter:
|
||||
|
||||
$ python portfolio_optimization.py
|
||||
'''
|
||||
|
||||
from __future__ import division
|
||||
import os
|
||||
import pytz
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy.optimize import minimize
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import datetime
|
||||
|
||||
from catalyst.api import record, symbol, symbols, order_target_percent
|
||||
from catalyst.utils.run_algo import run_algorithm
|
||||
|
||||
np.set_printoptions(threshold='nan', suppress=True)
|
||||
|
||||
|
||||
def initialize(context):
|
||||
# Portfolio assets list
|
||||
context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
|
||||
'xmr_usdt')
|
||||
context.nassets = len(context.assets)
|
||||
# Set the time window that will be used to compute expected return
|
||||
# and asset correlations
|
||||
context.window = 180
|
||||
# Set the number of days between each portfolio rebalancing
|
||||
context.rebalance_period = 30
|
||||
context.i = 0
|
||||
|
||||
|
||||
def handle_data(context, data):
|
||||
# Only rebalance at the beggining of the algorithm execution and
|
||||
# every multiple of the rebalance period
|
||||
if context.i == 0 or context.i%context.rebalance_period == 0:
|
||||
n = context.window
|
||||
prices = data.history(context.assets, fields='price',
|
||||
bar_count=n+1, frequency='1d')
|
||||
pr = np.asmatrix(prices)
|
||||
t_prices = prices.iloc[1:n+1]
|
||||
t_val = t_prices.values
|
||||
tminus_prices = prices.iloc[0:n]
|
||||
tminus_val = tminus_prices.values
|
||||
# Compute daily returns (r)
|
||||
r = np.asmatrix(t_val/tminus_val-1)
|
||||
# Compute the expected returns of each asset with the average
|
||||
# daily return for the selected time window
|
||||
m = np.asmatrix(np.mean(r, axis=0))
|
||||
# ###
|
||||
stds = np.std(r, axis=0)
|
||||
# Compute excess returns matrix (xr)
|
||||
xr = r - m
|
||||
# Matrix algebra to get variance-covariance matrix
|
||||
cov_m = np.dot(np.transpose(xr),xr)/n
|
||||
# Compute asset correlation matrix (informative only)
|
||||
corr_m = cov_m/np.dot(np.transpose(stds),stds)
|
||||
|
||||
# Define portfolio optimization parameters
|
||||
n_portfolios = 50000
|
||||
results_array = np.zeros((3+context.nassets,n_portfolios))
|
||||
for p in xrange(n_portfolios):
|
||||
weights = np.random.random(context.nassets)
|
||||
weights /= np.sum(weights)
|
||||
w = np.asmatrix(weights)
|
||||
p_r = np.sum(np.dot(w,np.transpose(m)))*365
|
||||
p_std = np.sqrt(np.dot(np.dot(w,cov_m),np.transpose(w)))*np.sqrt(365)
|
||||
|
||||
#store results in results array
|
||||
results_array[0,p] = p_r
|
||||
results_array[1,p] = p_std
|
||||
#store Sharpe Ratio (return / volatility) - risk free rate element
|
||||
#excluded for simplicity
|
||||
results_array[2,p] = results_array[0,p] / results_array[1,p]
|
||||
i = 0
|
||||
for iw in weights:
|
||||
results_array[3+i,p] = weights[i]
|
||||
i += 1
|
||||
|
||||
#convert results array to Pandas DataFrame
|
||||
results_frame = pd.DataFrame(np.transpose(results_array),
|
||||
columns=['r','stdev','sharpe']+context.assets)
|
||||
#locate position of portfolio with highest Sharpe Ratio
|
||||
max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
|
||||
#locate positon of portfolio with minimum standard deviation
|
||||
min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
|
||||
|
||||
#order optimal weights for each asset
|
||||
for asset in context.assets:
|
||||
if data.can_trade(asset):
|
||||
order_target_percent(asset, max_sharpe_port[asset])
|
||||
|
||||
#create scatter plot coloured by Sharpe Ratio
|
||||
plt.scatter(results_frame.stdev,results_frame.r,c=results_frame.sharpe,cmap='RdYlGn')
|
||||
plt.xlabel('Volatility')
|
||||
plt.ylabel('Returns')
|
||||
plt.colorbar()
|
||||
#plot red star to highlight position of portfolio with highest Sharpe Ratio
|
||||
plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker='o',color='b',s=200)
|
||||
#plot green star to highlight position of minimum variance portfolio
|
||||
plt.show()
|
||||
print(max_sharpe_port)
|
||||
record(pr=pr,r=r, m=m, stds=stds ,max_sharpe_port=max_sharpe_port, corr_m=corr_m)
|
||||
context.i += 1
|
||||
|
||||
|
||||
def analyze(context=None, results=None):
|
||||
# Form DataFrame with selected data
|
||||
data = results[['pr','r','m','stds','max_sharpe_port','corr_m','portfolio_value']]
|
||||
|
||||
# Save results in CSV file
|
||||
filename = os.path.splitext(os.path.basename(__file__))[0]
|
||||
data.to_csv(filename + '.csv')
|
||||
|
||||
|
||||
# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
|
||||
start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc)
|
||||
results = run_algorithm(initialize=initialize,
|
||||
handle_data=handle_data,
|
||||
analyze=analyze,
|
||||
start=start,
|
||||
end=end,
|
||||
exchange_name='poloniex',
|
||||
capital_base=100000, )
|
||||
@@ -546,6 +546,7 @@ only bought bitcoin every chance it got.
|
||||
sudo apt install python-tk
|
||||
|
||||
|
||||
.. _history:
|
||||
|
||||
Access to previous prices using ``history``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
+791
-334
File diff suppressed because it is too large
Load Diff
+24
-25
@@ -85,7 +85,7 @@ Bug Fixes
|
||||
- Fixed issue with sell orders in backtesting
|
||||
- Fixed data frequency issues with data.history() in backtesting
|
||||
- Fixed an issue with can_trade()
|
||||
- Reduced the commission and slippage values to account for lower volume
|
||||
- Reduced the commission and slippage values to account for lower volume
|
||||
transactions
|
||||
|
||||
Build
|
||||
@@ -97,17 +97,17 @@ Documentation
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
- Improved installation notes for Windows C++ compiler and Conda
|
||||
- Addition of
|
||||
- Addition of
|
||||
`Jupyter Notebook guide <https://enigmampc.github.io/catalyst/jupyter.html>`_
|
||||
- Addition of
|
||||
- Addition of
|
||||
`Live Trading page <https://enigmampc.github.io/catalyst/live-trading.html>`_
|
||||
- Addition of
|
||||
- Addition of
|
||||
`Videos page <https://enigmampc.github.io/catalyst/videos.html>`_
|
||||
- Addition of
|
||||
- Addition of
|
||||
`Resources page <https://enigmampc.github.io/catalyst/resources.html>`_
|
||||
- Addition of `Development Guidelines
|
||||
- Addition of `Development Guidelines
|
||||
<https://enigmampc.github.io/catalyst/development-guidelines.html>`_
|
||||
- Addition of
|
||||
- Addition of
|
||||
`Release Notes <https://enigmampc.github.io/catalyst/releases.html>`_
|
||||
- Updated code docstrings
|
||||
|
||||
@@ -158,10 +158,10 @@ Bug Fixes
|
||||
~~~~~~~~~
|
||||
|
||||
- Fixed OS-dependent path issue in data bundle
|
||||
- Changed handling of empty ``auth.json``, instead of throwing an error for
|
||||
- Changed handling of empty ``auth.json``, instead of throwing an error for
|
||||
missing file
|
||||
- Updated ``etc/python2.7-environment.yml`` to work with Catalyst version 0.3
|
||||
- Updated ``catalyst/examples/buy_and_hodl.py`` and
|
||||
- Updated ``catalyst/examples/buy_and_hodl.py`` and
|
||||
``catalyst/examples/buy_low_sell_high.py`` to work with Catalyst version 0.3
|
||||
|
||||
|
||||
@@ -181,18 +181,18 @@ Version 0.2.dev5
|
||||
^^^^^^^^^^^^^^^^
|
||||
**Release Date**: 2017-10-03
|
||||
|
||||
- Fixes bug in data.history function that was formatting 'volume' data as
|
||||
integers, now they are returned as floats with up to 9 decimals of precision.
|
||||
- Fixes bug in data.history function that was formatting 'volume' data as
|
||||
integers, now they are returned as floats with up to 9 decimals of precision.
|
||||
Data bundles redone.
|
||||
|
||||
Version 0.2.dev4
|
||||
Version 0.2.dev4
|
||||
^^^^^^^^^^^^^^^^
|
||||
|
||||
**Release Date**: 2017-09-20
|
||||
|
||||
- Fixes bug in the pricing resolution of 1-minute data, now set to 8 decimal
|
||||
- Fixes bug in the pricing resolution of 1-minute data, now set to 8 decimal
|
||||
places. Pricing resolution of daily data remains set to 9 decimal places.
|
||||
- The current data bundle takes 340MB compressed for download, and 460MB
|
||||
- The current data bundle takes 340MB compressed for download, and 460MB
|
||||
uncompressed on disk for Catalyst to use.
|
||||
|
||||
Version 0.2.dev3
|
||||
@@ -202,14 +202,14 @@ Version 0.2.dev3
|
||||
|
||||
- 1-minute resolution OHLCV data bundle for backtesting from Poloniex exchange
|
||||
- Implementation of trading of fractional crypto assets (i.e. 0.01 BTC)
|
||||
- Minimum trade size of a coin can be configured on a per-coin basis, defaults
|
||||
to 0.00000001 in backtesting (most exchanges set the minimum trade to larger
|
||||
- Minimum trade size of a coin can be configured on a per-coin basis, defaults
|
||||
to 0.00000001 in backtesting (most exchanges set the minimum trade to larger
|
||||
amounts, which will impact live trading)
|
||||
- Increased pricing resolution from 3 to 9 decimal places
|
||||
- The current data bundle takes 40MB compressed for download, and 99MB
|
||||
- The current data bundle takes 40MB compressed for download, and 99MB
|
||||
uncompressed on disk for Catalyst to use.
|
||||
|
||||
Version 0.2.dev2
|
||||
Version 0.2.dev2
|
||||
^^^^^^^^^^^^^^^^
|
||||
|
||||
**Release Date**: 2017-09-07
|
||||
@@ -225,15 +225,15 @@ Version 0.2.dev1
|
||||
|
||||
- Comprehensive trading functionality against exchanges Bitfinex and Bittrex.
|
||||
- Support for all trading pairs available on each exchange.
|
||||
- Multiple algorithms can trade simultaneously against a single exchange
|
||||
- Multiple algorithms can trade simultaneously against a single exchange
|
||||
using the same account.
|
||||
- Each algorithm has a persisted state (i.e. algorithm can be stopped and
|
||||
restarted preserving the state without data loss) that tracks all open
|
||||
- Each algorithm has a persisted state (i.e. algorithm can be stopped and
|
||||
restarted preserving the state without data loss) that tracks all open
|
||||
orders, executed transactions and portfolio positions.
|
||||
|
||||
- Minute by minute portfolio performance metrics.
|
||||
|
||||
- Daily summary performance statistics compatible with pyfolio, a Python
|
||||
- Daily summary performance statistics compatible with pyfolio, a Python
|
||||
library for performance and risk analysis of financial portfolios
|
||||
|
||||
Version 0.1.dev9
|
||||
@@ -241,13 +241,13 @@ Version 0.1.dev9
|
||||
|
||||
**Release Date**: 2017-08-28
|
||||
|
||||
- Retrieval of crypto benchmark from bundle, instead of hitting Poloniex
|
||||
- Retrieval of crypto benchmark from bundle, instead of hitting Poloniex
|
||||
exchange directly
|
||||
- Change of bundle storage provider from Dropbox to AWS
|
||||
- Fix issue with 1/1000 scaling issue of prices in bundle
|
||||
|
||||
Version 0.1.dev8
|
||||
^^^^^^^^^^^^^^^^
|
||||
^^^^^^^^^^^^^^^^
|
||||
|
||||
**Release Date**: 2017-08-18
|
||||
|
||||
@@ -267,4 +267,3 @@ Version 0.1.dev6
|
||||
**Release Date**: 2017-07-13
|
||||
|
||||
- Initial public release
|
||||
|
||||
|
||||
+17
-1
@@ -32,7 +32,9 @@ Where things don't:
|
||||
Backtesting a Strategy
|
||||
----------------------
|
||||
|
||||
This algorithm is based on a simple momentum strategy. When the cryptoasset
|
||||
This is the first video of a two-part series on using Catalyst for algorithmic
|
||||
trading. This video implements a simple momentum strategy based on
|
||||
`mean reversion <example_algos.html#mean_reversion>`_: when the cryptoasset
|
||||
goes up quickly, we’re going to buy; when it goes down quickly, we’re going to
|
||||
sell. Hopefully, we’ll ride the waves.
|
||||
|
||||
@@ -40,3 +42,17 @@ sell. Hopefully, we’ll ride the waves.
|
||||
|
||||
<iframe width="560" height="315" src="https://www.youtube.com/embed/JOBRwst9jUY" frameborder="0" allowfullscreen></iframe>
|
||||
|
||||
|
|
||||
|
|
||||
Live Trading a Strategy
|
||||
-----------------------
|
||||
|
||||
This is the second part of the two-part series on using Catalyst for algorithmic
|
||||
trading. Having backtested `our strategy <example_algos.html#mean_reversion>`_
|
||||
in the previous video, we now take it to trade live against the Bittrex exchange.
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<iframe width="560" height="315" src="https://www.youtube.com/embed/NupiE-Xuglw" frameborder="0" allowfullscreen></iframe>
|
||||
|
|
||||
|
|
||||
Reference in New Issue
Block a user