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Topic: Quantopian -- The basics |
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| Quantopian -- The basics |
The Algorithm Bare Bone:
# Import Algorithm API
import quantopian.algorithm as algo
def initialize(context):
# Initialize algorithm parameters
context.minute_count = 0
context.day_count = 0
context.minutely_message = "Minute {}."
context.daily_message = "Day {}."
context.weekly_message = "Time to place some trades!"
# Schedule rebalance function
algo.schedule_function(
rebalance,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open()
)
def handle_data(context, data):
context.minute_count += 1
if context.minute_count%60 == 0:
log.info(context.minutely_message, context.minute_count)
def before_trading_start(context, data):
# Execute any daily actions that need to happen
# before the start of a trading session
context.day_count += 1
log.info(context.daily_message, context.day_count)
def rebalance(context, data):
# Execute rebalance logic
log.info(context.weekly_message)
The output:
2019-01-02 05:45 before_trading_start:29 INFO Day 1.
2019-01-02 07:30 handle_data:23 INFO Minute 60.
2019-01-02 08:30 handle_data:23 INFO Minute 120.
2019-01-02 09:30 handle_data:23 INFO Minute 180.
2019-01-02 10:30 handle_data:23 INFO Minute 240.
2019-01-02 11:30 handle_data:23 INFO Minute 300.
2019-01-02 12:30 handle_data:23 INFO Minute 360.
2019-01-03 05:45 before_trading_start:29 INFO Day 2.
2019-01-03 07:00 handle_data:23 INFO Minute 420.
2019-01-03 08:00 handle_data:23 INFO Minute 480.
2019-01-03 09:00 handle_data:23 INFO Minute 540.
2019-01-03 10:00 handle_data:23 INFO Minute 600.
2019-01-03 11:00 handle_data:23 INFO Minute 660.
2019-01-03 12:00 handle_data:23 INFO Minute 720.
2019-01-03 13:00 handle_data:23 INFO Minute 780.
2019-01-04 05:45 before_trading_start:29 INFO Day 3.
2019-01-04 07:30 handle_data:23 INFO Minute 840.
2019-01-04 08:30 handle_data:23 INFO Minute 900.
2019-01-04 09:30 handle_data:23 INFO Minute 960.
2019-01-04 10:30 handle_data:23 INFO Minute 1020.
2019-01-04 11:30 handle_data:23 INFO Minute 1080.
2019-01-04 12:30 handle_data:23 INFO Minute 1140.
2019-01-07 05:45 before_trading_start:29 INFO Day 4.
2019-01-07 06:31 rebalance:34 INFO Time to place some trades!
2019-01-07 07:00 handle_data:23 INFO Minute 1200.
2019-01-07 08:00 handle_data:23 INFO Minute 1260.
2019-01-07 09:00 handle_data:23 INFO Minute 1320.
2019-01-07 10:00 handle_data:23 INFO Minute 1380.
2019-01-07 11:00 handle_data:23 INFO Minute 1440.
2019-01-07 12:00 handle_data:23 INFO Minute 1500.
2019-01-07 13:00 handle_data:23 INFO Minute 1560.
2019-01-08 05:45 before_trading_start:29 INFO Day 5.
2019-01-08 07:30 handle_data:23 INFO Minute 1620.
2019-01-08 08:30 handle_data:23 INFO Minute 1680.
2019-01-08 09:30 handle_data:23 INFO Minute 1740.
2019-01-08 10:30 handle_data:23 INFO Minute 1800.
2019-01-08 11:30 handle_data:23 INFO Minute 1860.
2019-01-08 12:30 handle_data:23 INFO Minute 1920.
2019-01-09 05:45 before_trading_start:29 INFO Day 6.
2019-01-09 07:00 handle_data:23 INFO Minute 1980.
2019-01-09 08:00 handle_data:23 INFO Minute 2040.
2019-01-09 09:00 handle_data:23 INFO Minute 2100.
2019-01-09 10:00 handle_data:23 INFO Minute 2160.
2019-01-09 11:00 handle_data:23 INFO Minute 2220.
2019-01-09 12:00 handle_data:23 INFO Minute 2280.
2019-01-09 13:00 handle_data:23 INFO Minute 2340.
2019-01-10 05:45 before_trading_start:29 INFO Day 7.
2019-01-10 07:30 handle_data:23 INFO Minute 2400.
2019-01-10 08:30 handle_data:23 INFO Minute 2460.
2019-01-10 09:30 handle_data:23 INFO Minute 2520.
2019-01-10 10:30 handle_data:23 INFO Minute 2580.
2019-01-10 11:30 handle_data:23 INFO Minute 2640.
2019-01-10 12:30 handle_data:23 INFO Minute 2700.
2019-01-11 05:45 before_trading_start:29 INFO Day 8.
2019-01-11 07:00 handle_data:23 INFO Minute 2760.
2019-01-11 08:00 handle_data:23 INFO Minute 2820.
2019-01-11 09:00 handle_data:23 INFO Minute 2880.
2019-01-11 10:00 handle_data:23 INFO Minute 2940.
2019-01-11 11:00 handle_data:23 INFO Minute 3000.
2019-01-11 12:00 handle_data:23 INFO Minute 3060.
2019-01-11 13:00 handle_data:23 INFO Minute 3120.
2019-01-14 05:45 before_trading_start:29 INFO Day 9.
2019-01-14 06:31 rebalance:34 INFO Time to place some trades!
What's going on here?
Function handle_data(context, data) (-- optional) has been triggered by system every minute;
Function before_trading_start(context, data) has been triggered by system (45 minutes) before trading start;
Function rebalance(context, data) has been triggered by Scheduler by its own specific rules.
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WebSpider
member
offline  |
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| posts: |
147 |
| joined: |
06/29/2006 |
| from: |
Seattle, WA |
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| The Basic Objects and Functions |
Object context:
context is a Python dictionary used for maintaining state by attaching any property:
context.some_property = some_value
Object assets:
assets is a Asset or a list of Assets which represents security.
Find security asset by name:
assets = symbol('AAPL')
assets = [symbol('AAPL'), symbol('MSFT')]
assets = symbols('AAPL', 'MSFT')
Find security asset by id:
# AAPL
assets = sid(24)
# AAPL, MSFT, and SPY
assets = [sid(24), sid(5061), sid(8554)]
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WebSpider
member
offline |
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| posts: |
147 |
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Seattle, WA |
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| Object Data |
data.current(assets, fields)
This method returns the current value of the given assets for the given fields at the current algorithm time.
Parameter fields:
'price' -- returns the last known close price, NaN if not founded
'last_traded' -- returns the date of the last trade event
'open' -- return the relevant information for the current trade bar
'high' -- return the relevant information for the current trade bar
'low' -- return the relevant information for the current trade bar
'close' -- return the relevant information for the current trade bar
'volume' -- returns the trade volume, 0 if there is no trade this minute
'contract' -- returns the current active contract
Example:
price = data.current(symbol('AAPL'), 'price');
data.history(assets, fields, bar_count, frequency)
This method returns a window of data for the given assets and fields.
Parameter fields:
'price' -- returns the last known close price, NaN if not founded
'open' -- return the relevant information for the current trade bar
'high' -- return the relevant information for the current trade bar
'low' -- return the relevant information for the current trade bar
'close' -- return the relevant information for the current trade bar
'volume' -- returns the trade volume, 0 if there is no trade this minute
Parameter frequency:
'1m' -- for minutely data
'1d' -- for daily data
Examples (DAILY):
# returns the current price
price_history = data.history(assets, "price", 1, "1d");
# returns yesterday's close price and the current price
price_history = data.history(assets, "price", 2, "1d");
# returns the prices for the previous 5 days and the current price
price_history = data.history(assets, "price", 6, "1d");
# returns the volume since the current day's open, even if it is partial
price_history = data.history(assets, "volume", 1, "1d");
Examples (MINUTELY):
# returns the current price
price_history = data.history(assets, "price", 1, "1m");
# returns the previous minute's close price and the current price
price_history = data.history(assets, "price", 2, "1m");
# returns the prices for the previous 5 minutes and the current price
price_history = data.history(assets, "price", 6, "1m");
# returns the volume for the previous 60 minutes
price = data.history(assets, "volume", 60, "1m");
Example -- Up or down compared to previouse day's close price?
def initialize(context):
# AAPL, MSFT, and SPY
context.securities = [sid(24), sid(5061), sid(8554)]
def handle_data(context, data):
price_history = data.history(context.securities, fields="price", bar_count=2, frequency="1d")
for s in context.securities:
prev_close = price_history[s][-2];
curr_price = price_history[s][-1];
if curr_price > prev_close:
print(s, prev_close, curr_price, ' --> UP');
else:
print(s, prev_close, curr_price, ' --> DOWN');
Example -- VWAP (Volume Weighted Average Price)
def initialize(context):
# AAPL, MSFT, and SPY
context.securities = [sid(24), sid(5061), sid(8554)]
def vwap(prices, volumes):
return (prices * volumes).sum() / volumes.sum()
def handle_data(context, data):
hist = data.history(context.securities, fields=["price", "volume"], bar_count=30, frequency="1d")
vwap_10 = vwap(hist["price"][-10:], hist["volume"][-10:])
vwap_30 = vwap(hist["price"], hist["volume"])
for s in context.securities:
if vwap_10[s] > vwap_30[s] :
order(s, 100)
elif vwap_10[s] < vwap_30[s] :
order(s, -10)
Other data's methods:
data.can_trade(assets) -- For the given asset or iterable of assets, returns true if the security has a known last price
data.is_stale(assets) -- For the given asset or iterable of assets, returns true if the asset has ever traded and there is no trade data for the current simulation time |
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WebSpider
member
offline |
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| posts: |
147 |
| joined: |
06/29/2006 |
| from: |
Seattle, WA |
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| Ordering Methods |
order_optimal_portfolio(objective, constraints)
This method places one or more orders by calculating a new optimal portfolio based on the objective and constraints.
order(asset, amount, style=OrderType)
This methods places an order for the specified asset and the specified amount of shares (equities) or contracts (futures).
Parameter OrderType:
style=MarketOrder(exchange)
style=StopOrder(stop_price, exchange)
style=LimitOrder(limit_price, exchange)
style=StopLimitOrder(limit_price=price1, stop_price=price2, exchange)
order_value(asset, amount, style=OrderType)
Place an order by desired value rather than desired number of shares or contracts.
order_percent(asset, amount, style=OrderType)
Places an order in the specified asset corresponding to the given percent of the current portfolio value, which is the sum of the positions value and ending cash balance. Placing a negative percent order will result in selling the given percent of the current portfolio value. Orders are always truncated to whole shares or contracts. Percent must be expressed as a decimal (0.50 means 50%).
Example
order_percent(symbol('AAPL'), .5) will order AAPL shares worth 50% of current portfolio value. If AAPL is $100/share and the portfolio value is $2000, this buys 10 shares (discarding slippage and transaction cost).
order_target(asset, amount, style=OrderType)
Places an order to maintain the target number of shares in portfolio. Placing a negative target order will result in a short position equal to the negative number specified.
Example
If the current portfolio has 5 shares of AAPL and the target is 20 shares, order_target(symbol('AAPL'), 20) orders 15 more shares of AAPL.
order_target_value(asset, amount, style=OrderType)
Places an order to adjust a position to a target value. Placing a negative target order will result in a short position equal to the negative target value.
Example
If the current portfolio holds $500 worth of AAPL and the target is $2000, order_target_value(symbol('AAPL'), 2000) orders $1500 worth of AAPL (rounded down to the nearest share).
cancel_order(order)
Attempts to cancel the specified order.
Parameters
order: Can be the order_id as a string or the order object.
get_open_orders(sid)
If asset is None or not specified, returns all open orders. If asset is specified, returns open orders for that asset
Parameters
sid: An Equity object or a Future object. Can also be None.
Returns
If asset is unspecified or None, returns a dictionary keyed by asset ID. The dictionary contains a list of orders for each ID, oldest first. If an asset is specified, returns a list of open orders for that asset, oldest first.
get_order(order)
Returns the specified order. The order object is discarded at the end of handle_data.
Parameters
order: Can be the order_id as a string or the order object.
Returns
An order object that is read/writeable but is discarded at the end of handle_data.
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posted on: 04/29/2019 10:45:33 PM
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