Strategy Builder Overview
System Overview
Demo Video
Writing a Strategy
When writing a strategy, the first step is to import the Strategy and TargetAllocation classes like so:
from surmount.base_class import Strategy, TargetAllocation
Next, import the technical indicators and data sources that you want to use for your strategy. For more information, scroll down to the example strategies or click on the links below:
After that, define your strategy class, interval, assets, and data. The interval can be one of the following: 1min, 5min, 1hour, 4hour, 1day. The data list should be a list of the data classes you imported earlier.
class TradingStrategy(Strategy):
def __init__(self):
self.tickers = ["AAPL", ...]
self.data_list = [...]
@property
def interval(self):
return "1day"
@property
def assets(self):
return self.tickers
@property
def data(self):
return self.data_list
Finally, define your strategy in the run() method. The data object is a Python dictionary containing OHLCV data as well as data from all of the sources you added to data_list above. This method needs to return a TargetAllocation object, which is a dictionary with the keys being tickers and values being decimal allocations that add up to 1 at the most.
def run(self, data):
allocation_dict = {i: 1/len(self.tickers) for i in self.tickers}
# WRITE YOUR STRATEGY LOGIC HERE
return TargetAllocation(allocation_dict)
Example Strategies
Here is a simple strategy that checks the latest insider trade for sales:
from surmount.base_class import Strategy, TargetAllocation
from surmount.technical_indicators import RSI, EMA, SMA, MACD, MFI, BB
from surmount.logging import log
from surmount.data import Asset, InstitutionalOwnership, InsiderTrading
class TradingStrategy(Strategy):
def __init__(self):
self.tickers = ["SPY", "QQQ", "AAPL", "GOOGL"]
self.data_list = [InstitutionalOwnership(i) for i in self.tickers]
self.data_list += [InsiderTrading(i) for i in self.tickers]
@property
def interval(self):
return "1day"
@property
def assets(self):
return self.tickers
@property
def data(self):
return self.data_list
def run(self, data):
allocation_dict = {i: 1/len(self.tickers) for i in self.tickers}
for i in self.data_list:
if tuple(i)[0]=="insider_trading":
if data[tuple(i)] and len(data[tuple(i)])>0:
if "Sale" in data[tuple(i)][-1]['transactionType']:
allocation_dict[tuple(i)[1]] = 0
return TargetAllocation(allocation_dict)
This is an interesting strategy that buys QQQ midday if there is a V shape in the last 3 candles:
from surmount.base_class import Strategy, TargetAllocation
from surmount.technical_indicators import RSI, EMA, SMA, MACD, MFI, BB
from surmount.logging import log
class TradingStrategy(Strategy):
@property
def assets(self):
return ["QQQ"]
@property
def interval(self):
return "1hour"
def run(self, data):
d = data["ohlcv"]
qqq_stake = 0
if len(d)>3 and "13:00" in d[-1]["QQQ"]["date"]:
v_shape = d[-2]["QQQ"]["close"]<d[-3]["QQQ"]["close"] and d[-1]["QQQ"]["close"]>d[-2]["QQQ"]["close"]
log(str(v_shape))
if v_shape:
qqq_stake = 1
return TargetAllocation({"QQQ": qqq_stake})
This is a strategy that buys $VIRT when there is a spike in SPY trading volume:
from surmount.base_class import Strategy, TargetAllocation
from surmount.logging import log
from surmount.data import Asset, InstitutionalOwnership
import pandas_ta as ta
import pandas as pd
def SMAVol(ticker, data, length):
'''Calculate the moving average of trading volume
:param ticker: a string ticker
:param data: data as provided from the OHLCV data function
:param length: the window
:return: list with float SMA
'''
close = [i[ticker]["volume"] for i in data]
d = ta.sma(pd.Series(close), length=length)
if d is None:
return None
return d.tolist()
class TradingStrategy(Strategy):
def __init__(self):
self.tickers = ["VIRT"]
self.data_list = []
@property
def interval(self):
return "1day"
@property
def assets(self):
return self.tickers
@property
def data(self):
return self.data_list
def run(self, data):
vols = [i["VIRT"]["volume"] for i in data["ohlcv"]]
smavols = SMAVol("VIRT", data["ohlcv"], 40)
smavols2 = SMAVol("VIRT", data["ohlcv"], 10)
if len(vols)==0:
return TargetAllocation({})
if smavols2[-1]/smavols[-1]-1>0:
out = smavols2[-1]/smavols[-1]-1
else: out = 0
return TargetAllocation({"VIRT": min(0.9, (out*10)**(0.5))})
This strategy uses pyramiding and Bollinger bands to build long and short positions on $QQQ.:
from surmount.base_class import Strategy, TargetAllocation
from surmount.technical_indicators import SMA, BB
from surmount.logging import log
class TradingStrategy(Strategy):
@property
def assets(self):
return ["QQQ", "SQQQ"]
@property
def interval(self):
return "1hour"
def run(self, data):
holdings = data["holdings"]
data = data["ohlcv"]
sqqq_stake = 0
qqq_stake = 0
qqq_bbands = BB("QQQ", data, 20, 1.4)
qqq_ma = SMA("QQQ", data, 5)
if len(data)<20:
return TargetAllocation({})
current_price = data[-1]["QQQ"]['close']
if qqq_bbands is not None and current_price < qqq_bbands['lower'][-1] and qqq_ma[-1]>qqq_ma[-2]:
log("going long")
if holdings["QQQ"] >= 0:
qqq_stake = min(1, holdings["QQQ"]+0.1)
else:
qqq_stake = 0.4
elif qqq_bbands is not None and current_price > qqq_bbands['upper'][-1]:
log("going short")
if holdings["SQQQ"] >= 0:
sqqq_stake = min(1, holdings["SQQQ"]+0.075)
else:
sqqq_stake = 0.2
else:
log("meh")
qqq_stake = 0
sqqq_stake = 0
return TargetAllocation({"SQQQ": sqqq_stake, "QQQ": qqq_stake})