# Entering and exiting trades
Deciding to enter a trade is nothing but a True of False decision.
should_short() methods which must return a boolean at all times.
After making your mind about entering a trade, you need to come up with exact entry prices, and exit prices. Jesse uses
go_short() methods for that.
Return Type: bool
Assuming the position is currently close, should it open a long position.
def should_long(self): # return true if current candle is a bullish candle if self.close > self.open: return True return False
Return Type: bool
Assuming the position is currently close, should it open a short position.
def should_short(self): # return true if current candle is a bearish candle if self.close < self.open: return True return False
Obviously you cannot enter both a short and long position at the same time. Hence,
should_short() cannot return True at the same.
should_short() are for entering trades only. This means that they would get called on every new candle only if no position is open, and no order is active.
If you're looking to close trades dynamically, update_position() is what you're looking fore.
go_long() method you set your buy price (entry point), quantity (how much to buy), the stop-loss and take-profit (exit points) quantity and prices. The basic syntax is:
def go_long(self): self.buy = qty, entry_price self.stop_loss = qty, stop_loss_price self.take_profit = qty, take_profit_price
take_profit_price are placeholders, can be anything you want; but
self.take_profit are special variables that Jesse uses; they must be the same.
A working example would be:
def go_long(self): qty = 1 self.buy = qty, self.price self.stop_loss = qty, self.low - 10 self.take_profit = qty, self.high + 10
Smart ordering system
Notice that we did not have to define which order type to use. Jesse is smart enough to decide the type of the orders by itself.
For example if it is for a long position, here's how Jesse decides:
- MARKET order: if
entry_price == current_price
- LIMIT order: if
entry_price < current_price
- STOP order: if
entry_price > current_price
Same as go_long() but uses
self.sell for entry instead of
def go_short(self): self.sell = qty, entry_price self.stop_loss = qty, stop_loss_price self.take_profit = qty, take_profit_price
A working Example would be:
def go_short(self): qty = 1 # opens position with a MARKET order self.sell = qty, self.price self.stop_loss = qty, self.high + 10 self.take_profit = qty, self.low - 10
Return Type: bool
What this method is asking you is: Assuming that open position order has already been submitted but not executed yet, should it cancel it?
After submitting orders for opening new positions either you'll enter a position immediately with a market order, or have to wait until your limit/stop order gets filled. This method is used for the second scenario.
A good example would be for a trade we're trying to open a position when the price continues the uptrend:
def should_long(self): return True def go_long(self): qty = 1 entry = self.high + 2 self.buy = qty, entry self.stop_loss = qty, entry - 10 self.take_profit = qty, entry + 10
Since the entry price is above the current price, Jesse will submit a stop order for entering this trade. If the price indeed rises we'll be fine, but what if a new candle is passed, and the price goes down? Then we would want the previous order to be canceled and a new order submitted based on the high price of the new candle.
To do this, we'll have to specify the
def should_cancel(self): return True
In your strategy, you may need to do some checking before deciding whether or not the previous open-position order is still valid or has to be canceled.
should_cancel() only decides whether or not to cancel the entry order. It does not affect your exit (take-profit and stop-loss) orders.
# Entering and/or exiting at multiple points
So far we defined enter-once and exit-once strategy examples using only
go_short() methods. This may not be enough for your strategies.
For entering/exiting at one point we defined single tuples. To enter/exit at multiple points all you need to do is to use a list of tuples instead.
Example of taking profit at two points:
def go_long(): qty = 1 self.buy = qty, 100 self.stop_loss = qty, 80 # take-profit at two points self.take_profit = [ (qty/2, 120), (qty/2, 140) ]
We could do the same for
self.stop_loss if it makes sense in your strategy.
Example of entering the trade at two points:
def go_long(): qty = 1 # open position at $120 and increase it at $140 self.buy = [ (qty/2, 120), (qty/2, 140) ] self.stop_loss = qty, 100 self.take_profit = qty, 160
What if we're not aware of our exact exit point at the time of entering the trade? For instance, it is a common case in trend-following strategies to exit when the trend has stopped.
The next section introduces the concept of events to fulfill this need.
As explained in the flowchart, this is the first method that gets called when a new candle is received. It is used for updating
self.vars (custom variables) or any other action you might have in mind that needs to be done before your strategy gets executed.
See also: vars
Assuming that there's an open position, this method is used to update exit points or to add to the size of the position if needed.
If your strategy exits dynamically (for example if at the time of entering the trade you don't know the take-profit price) then you definitely need to use
Example #1: Exiting the trade by implementing a trailing stop for take-profit:
def update_position(self): qty = self.position.qty # set stop-loss price $10 away from the high/low of the current candle if self.is_long: self.take_profit = qty, self.high - 10 else: self.take_profit = qty, self.low + 10
Example #2: Liquidating the open position at a certain condition. In this case, we liquidate if we're in a long trade and the RSI reaches 100:
def update_position(self): if self.is_long and ta.rsi(self.candles) == 100: self.liquidate()
Example #3: Double the size of my long position if the RSI shows oversold and I'm sitting at more than 5% profit:
def update_position(self): if self.is_long: if self.position.pnl_percentage > 5 and ta.rsi(self.candles) < 30: # double the size of the already open position at current price (with a MARKET order) self.buy = self.position.qty, self.price
__init__ is not a new concept. It's the constructor of a Python class. Jesse strategies are Python classes, hence you may use the
__init__ method for actions that need to be performed in the beginning of a strategy and only for once.
You could say
__init__ is the opposite of the terminate() method in a Jesse strategy.
Remember to begin
__init__ method's content with a
super().__init__() call, otherwise you will get an error.
def __init__(self): super().__init__() print('initiated the strategy class')
There are cases where you need to tell Jesse to perform a task right before terminating (like finishing the backtest simulation). Examples of a such task would be to log a value, or save a machine learning model.
You could say
terminate is the opposite of the __init__ method in a Jesse strategy.
def terminate(self): print('backtest is done')