def test_backtest():
"""
Ensures that the backtest function works on all the registered strategies, with their default parameter values
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
# Simulate custom indicator
sample["custom"] = np.random.random((sample.shape[0], )) * 100
for strategy in STRATEGY_MAPPING.keys():
if strategy == "sentiment":
data = get_yahoo_data("TSLA", "2020-01-01", "2020-07-04")
sentiments = get_bt_news_sentiment(keyword="tesla", page_nums=2)
cerebro = backtest(strategy,
data,
sentiments=sentiments,
senti=0.4,
plot=False)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy)
assert cerebro is not None, errmsg
else:
cerebro = backtest(strategy, sample, plot=False)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy)
assert cerebro is not None, errmsg
def test_backtest():
"""
Ensures that the backtest function works on all the registered strategies, with their default parameter values
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
# Simulate custom indicator
sample["custom"] = np.random.random((sample.shape[0], )) * 100
for strategy in STRATEGY_MAPPING.keys():
if strategy == "sentiment":
data = get_yahoo_data("TSLA",
"2020-01-01",
"2020-07-04",
dividends=True)
# use cached data instead of scraping for tests purposes.
# sentiments = get_bt_news_sentiment(keyword="tesla", page_nums=2)
with open(SENTI_PKL, "rb") as handle:
sentiments = pickle.load(handle)
cerebro = backtest(strategy,
data,
sentiments=sentiments,
senti=0.4,
plot=False)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy)
assert cerebro is not None, errmsg
data_disclosures = get_stock_data(
"TSLA",
"2020-01-01",
"2020-09-30",
dividends=True, # source="phisix"
)
# sentiments_disclosures = get_disclosure_sentiment(
# stock_code="JFC",
# start_date="2020-07-01",
# end_date="2020-09-30",
# )
with open(DISCLOSURE_PKL, "rb") as handle_disclosures:
sentiments_disclosures = pickle.load(handle_disclosures)
cerebro_disclosures = backtest(
strategy,
data_disclosures,
sentiments=sentiments_disclosures,
senti=0.2,
plot=False,
)
errmsg_disclosures = "Backtest encountered error for strategy '{}'!".format(
strategy)
assert cerebro_disclosures is not None, errmsg_disclosures
else:
cerebro = backtest(strategy, sample, plot=False)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy)
assert cerebro is not None, errmsg
def test_multi_backtest():
"""
Test multi-strategy
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
cerebro = backtest("multi", sample, strats=SAMPLE_STRAT_DICT, plot=False)
assert cerebro is not None, "Backtest encountered error for strategy 'multi'!"
def conditional_confirmation_strat(predf,
condition='bionary',
ma_var=5,
plot_em=True):
'''
takes in a condition [ a bot interested in longing ] and only buys
when the price rises above a simple moving avg.
returns a backtest results dataframe
TAKES:
1. dataframe
2. condition column ( either > 0 OR ==True )
3. ma_var - moving avg value
4. plot_em - to plot or not to plot....
'''
# BOT LONG IS THE CONDITION THAT HAS TO BE TRUE in for a buy to be triggered
predf['bot_long'] = predf[
condition] > 0 #<---might have to change that to True
# CREATE A MOVING AVG TO SERVE AS VALIDATION/CONFIRMATION
predf['ma5'] = predf['Close'].rolling(ma_var).mean()
# CONDITIONS
predf['ma5_above'] = predf['Close'] > predf['ma5']
predf['bot_n_above'] = (predf['bot_long'] == True) & (predf['ma5_above']
== True)
predf['confirmation'] = (predf['bot_n_above']
== True) & (predf['bot_n_above'].shift() == False)
predf['validation'] = (predf['ma5_above']
== False) & (predf['ma5_above'].shift() == True)
predf['trac'] = False
predf['trigger'] = 0
# if conditions are met and we get confimation we get a tigger (predf[trigger])=1
for i in range(1, len(predf)):
if predf['confirmation'][i] == True:
predf['trac'][i] = True
predf['trigger'][i] = 1
elif predf['validation'][i] == True:
predf['trac'][i] = False
predf['trigger'][i] = -1
else:
predf['trac'][i] = predf['trac'][i - 1]
# CREATE A SCALED VERSION FOR PLOTTING
predf['trac_scale'] = predf['trac'].replace(True,
1).replace(1, predf.Close)
#plot
if plot_em:
predf[['Close', 'trac_scale']].iplot(theme='solar', fill=True)
results = backtest('ternary',
predf,
custom_column='trigger',
init_cash=1000.00,
plot=plot_em,
verbose=False)
return results
def test_backtest():
"""
Ensures that the backtest function works on all the registered strategies, with their default parameter values
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
for strategy in STRATEGY_MAPPING.keys():
cerebro = backtest(strategy, sample, plot=False)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy)
assert cerebro is not None, errmsg
def test_grid_backtest():
"""
Test grid search
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
cerebro = backtest(
"smac", sample, fast_period=[20, 25], slow_period=[40, 50], plot=False
)
assert (
cerebro is not None
), "Backtest encountered error doing grid search on SMAC!"
def test_backtest():
"""
Ensures that the backtest function works on all the registered strategies, with their default parameter values
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
for strategy in STRATEGY_MAPPING.keys():
if strategy == "sentiment":
data = get_yahoo_data("TSLA", "2020-01-01", "2020-06-10")
cerebro = backtest(
strategy, data, keyword="tesla", page_nums=2, senti=0.4
)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy
)
assert cerebro is not None, errmsg
else:
cerebro = backtest(strategy, sample, plot=False)
errmsg = "Backtest encountered error for strategy '{}'!".format(
strategy
)
assert cerebro is not None, errmsg
def buy_delta(df, THRESH, SELL):
df = delta_maker(df, THRESH)
BUY = 'delta_ab_thresh'
df['buy'] = (df[BUY].shift() == False) & (df[BUY] == True)
df['sell'] = (df[SELL].shift() == False) & (df[SELL] == True)
df = compile_signals(df)
result = backtest(df, plot_capital=True, strat_name='delta_experiments')
result['delta_thresh'] = THRESH
return result
def test_grid_backtest():
"""
Test grid search
"""
sample = pd.read_csv(SAMPLE_CSV, parse_dates=["dt"])
cerebro = backtest(
"smac",
sample,
fast_period=range(15, 30, 3),
slow_period=range(40, 55, 3),
plot=False,
)
assert cerebro is not None, "Backtest encountered error doing grid search on SMAC!"
def smac_backtest(df, var1, var2, plot=False):
'''
var1 = fast
var2 = slow
'''
result = backtest('smac',
df,
fast_period=var1,
slow_period=var2,
init_cash=1000,
plot=plot)
strat_name = 'smac'
result['strat_name'] = 'smac'
return result, strat_name
def rsi_backtest(df, var1, var2, plot=False):
'''
var1 - rsi_lower
var2 - rsi_upper
'''
result = backtest('rsi',
df,
rsi_lower=var1,
rsi_upper=var2,
init_cash=1000,
plot=plot)
strat_name = 'rsi-14:'
result['strat_name'] = strat_name
return result, strat_name
def macd_backtest(df, var1, var2, plot=False):
'''
var1 = fast_period - should be smaller than slow_upper
var2 = slow_period - shoudl be larget than fast_upper
'''
result = backtest(
'macd',
df,
fast_period=var1,
slow_period=var2,
signal_period=14,
#allowance = 6,
init_cash=1000,
plot=plot)
result['allowance'] = 6
result['signal_period'] = 14
strat_name = 'macd:'
result['strat_name'] = strat_name
return result, strat_name
def sma_up_validation(df,
sma_variable,
long_sma,
strat_name,
look_back,
rdf,
plot_capital=False):
'''
takes:
1.df
2.sma - float, when price is above this sma it will by ...
3.long_sma - float, will only buy if this is pointing up
4. results dataframe to append results to
returns:
final_acnt value for grid plot
'''
df = df[['close', 'low', 'open', 'high']]
df['longer_sma'] = df['close'].rolling(long_sma).mean().shift()
df['moving_avg'] = df['low'].rolling(sma_variable).mean().shift()
df['price_abv_ma'] = df['close'] > df['moving_avg']
df['buy_sma'] = (df['price_abv_ma'] == True) & (df['price_abv_ma'].shift()
== False)
df['sell'] = (df['price_abv_ma'] == False) & (df['price_abv_ma'].shift()
== True)
df['longer_up'] = (df['longer_sma'] > df['longer_sma'].shift(look_back))
df['buy'] = (df['buy_sma'] == True) & (df['longer_up'] == True)
#.....................................................................................................
df = compile_signals(df)
result = backtest(df,
strat_name='SMA-Price-CROSS',
plot_capital=plot_capital)
#print(result)
result['validation'] = sma_variable
result['sma_has_to_be_up'] = long_sma
rdf = rdf.append(result)
result['look_back'] = look_back
return result['final_acnt value'][0], result
def backtest_data_gdsr(df):
'''
Summary:
----------
Open json trading strategy file, test strategies through fastquant backtest
Params:
----------
df : dataframe with financial data
Outputs:
----------
none
'''
json_file_path = os.path.join(sys.path[0], 'bt_strats_params.json')
with open(json_file_path, encoding='utf-8', errors='ignore') as json_data:
strat_dict = json.load(json_data, strict=False)
for strat in strat_dict['strats']:
bt = backtest("multi", df, strats=strat, plot=True)
print(bt)
def atr_back_test(m,
df,
to_plot=False,
up_multiple=2,
dn_multiple=2,
plot_trac=False):
'''
this function does the backtest things
m is for model
'''
df['atr'] = pta.atr(df.High, df.Low, df.Close)
bdf = df[[m, 'atr', 'High', 'Low', 'Close']].dropna(axis=0)
#sns.heatmap(bdf.isnull())
bdf['atr_up'] = (bdf['Close'] + bdf['atr'] * up_multiple)
bdf['atr_down'] = (bdf['Close'] - bdf['atr'] * dn_multiple)
##### later create a function that makes variations of ups and downs
#### to_be an ATR -LOOP
# STRAT FUNCTION
### starts here...
'''llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll'''
'''inputs'''
buy = m #'Tree165473'
t_up = 'atr_up'
t_dn = 'atr_down'
#SO this needs to go into a function and will be part of the backtest loop for each model
# atr_targs and stop will be inputs so i can iterate through
#if bdf.index.name == 'date':
# bdf = bdf.reset_index()
bdf['trac'] = False
bdf['targ'] = bdf[t_up]
bdf['stop'] = bdf[t_dn]
for i in range(1, len(bdf)):
if (bdf[buy][i] == True) & (bdf['trac'][i - 1] == False):
bdf['trac'][i] = True
bdf['targ'][i] = bdf[t_up][i]
bdf['stop'][i] = bdf[t_dn][i]
elif (bdf['trac'][i - 1] == True) & (
(bdf['Low'][i] < bdf['stop'][i - 1]) |
(bdf['High'][i] > bdf['targ'][i - 1])):
bdf['trac'][i] = False
else:
bdf['trac'][i] = bdf['trac'][i - 1]
bdf['targ'][i] = bdf['targ'][i - 1]
bdf['stop'][i] = bdf['stop'][i - 1]
#hl(bdf)
#print(s)
#tit = (s + '_'+m)
if to_plot == True:
#sola(bdf[['stop','Close','targ']])#,title=tit)
bdf[['stop', 'Close', 'targ']].iplot(theme='solar', fill=True)
## todo- replace 'strat' with a varable
bdf['strat'] = 0
for i in range(1, len(bdf)):
if (bdf['trac'][i - 1] == False) & (bdf['trac'][i] == True):
bdf['strat'][i] = 1
elif (bdf['trac'][i - 1] == True) & (bdf['trac'][i] == False):
bdf['strat'][i] = -1
#hl(bdf)
#bdf =bdf.set_index('date')#.drop(['level_0','index'],axis=1)
bdf
#%matplotlib
results, history = backtest('ternary',
bdf,
custom_column='strat',
init_cash=1000,
plot=plot_trac,
verbose=False,
return_history=True)
#results['model'] = buy
#results['target']= target
#results['sheet'] = s
results['strat_type'] = 'atr_smacks'
results['up_multiple'] = up_multiple
results['dn_multiple'] = dn_multiple
#results = results.set_index('custom_column')
#results['buy'] = buy
#se
if plot_trac:
bdf['trac'] = bdf['trac'].replace(True, 1).replace(1, bdf.Close)
bdf[['Close', 'High', 'trac']].iplot(theme='solar', fill=True)
return results
from fastquant import backtest, get_stock_data
df = get_stock_data("AAAA", "2018-01-01", "2021-04-01")
res, plot = backtest('emac',
df,
init_cash=2000,
fast_period=10,
slow_period=30,
return_plot=True)
#plot.savefig('AAAA_emac.png')
from fastquant import backtest, get_stock_data
df = get_stock_data("AAAA", "2018-03-01", "2021-04-01")
############smac######################
res, plot = backtest('smac',
df,
init_cash=2000,
sell_prop=1,
buy_prop=1,
fast_period=10,
slow_period=30,
return_plot=True)
#plot.savefig('AAAA_smac.png')
def back_test_things(m, to_plot=False, up_multiple=2, dn_multiple=2):
'''
this function does the backtest things
m is for model
'''
df['atr'] = pta.atr(df.High, df.Low, df.Close)
bdf = df[[m, 'atr', 'High', 'Low', 'Close']].dropna(axis=0)
#sns.heatmap(bdf.isnull())
bdf['atr_up'] = (bdf['Close'] + bdf['atr'] * up_multiple)
bdf['atr_down'] = (bdf['Close'] - bdf['atr'] * dn_multiple)
##### later create a function that makes variations of ups and downs
#### to_be an ATR -LOOP
# STRAT FUNCTION
### starts here...
'''llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll'''
'''inputs'''
buy = m #'Tree165473'
t_up = 'atr_up'
t_dn = 'atr_down'
#SO this needs to go into a function and will be part of the backtest loop for each model
# atr_targs and stop will be inputs so i can iterate through
if bdf.index.name == 'date':
bdf = bdf.reset_index()
bdf['trac'] = False
bdf['targ'] = bdf[t_up]
bdf['stop'] = bdf[t_dn]
for i in range(1, len(bdf)):
if (bdf[buy][i] == True) & (bdf['trac'][i - 1] == False):
bdf['trac'][i] = True
bdf['targ'][i] = bdf[t_up][i]
bdf['stop'][i] = bdf[t_dn][i]
elif (bdf['trac'][i - 1] == True) & (
(bdf['Low'][i] < bdf['stop'][i - 1]) |
(bdf['High'][i] > bdf['targ'][i - 1])):
bdf['trac'][i] = False
else:
bdf['trac'][i] = bdf['trac'][i - 1]
bdf['targ'][i] = bdf['targ'][i - 1]
bdf['stop'][i] = bdf['stop'][i - 1]
#hl(bdf)
print(s)
tit = (s + '_' + m)
#if to_plot==True:
#sola(bdf[['stop','Close','targ']],title=tit)
## todo- replace 'strat' with a varable
bdf['strat'] = 0
for i in range(1, len(bdf)):
if (bdf['trac'][i - 1] == False) & (bdf['trac'][i] == True):
bdf['strat'][i] = 1
elif (bdf['trac'][i - 1] == True) & (bdf['trac'][i] == False):
bdf['strat'][i] = -1
#hl(bdf)
bdf = bdf.set_index('date') #.drop(['level_0','index'],axis=1)
bdf
#%matplotlib
results, history = backtest('ternary',
bdf,
custom_column='strat',
init_cash=1000,
plot=to_plot,
verbose=False,
return_history=True)
results['model'] = buy
results['target'] = target
results['sheet'] = s
results['up_multiple'] = up_multiple
results['dn_multiple'] = dn_multiple
#results = results.set_index('custom_column')
#results['buy'] = buy
#se
results.T
### so now i need to rename the thing after the strat and atr paramaters
####
##### this is ready to become a save_function... but
btpath = 'backtest_data/'
if not os.path.exists(btpath):
os.mkdir(btpath)
fname = 'results.csv'
if not os.path.exists(btpath + fname):
print('dosnt exitst')
rdf = results
rdf.to_csv(btpath + fname)
print('results.csv created')
else:
rdf = pd.read_csv(btpath + fname).drop('Unnamed: 0', axis=1)
print('already exists')
rdf = rdf.append(results)
print('apending')
rdf.to_csv(btpath + fname)
print('results.csv UPDATED!')
def get_strategy(symbol: str, model: str, initial_cash: int,
commission_percentage: float, short_sell: bool,
margin_percentage: float, investment_period: int):
try:
df = get_stock_data(symbol, investment_period)
df.rename(
columns={
"open_price": "Open",
"high_price": "High",
"low_price": "Low",
"close_price": "Close",
"volume": "Volume",
},
inplace=True,
)
next_n_trading_days = get_next_n_trading_days(investment_period)
preds = get_predictions(symbol, next_n_trading_days.min(),
next_n_trading_days.max(), model).to_frame()
df1 = preds.combine_first(df[["Close"]])
kwargs = {
"data": df1[-investment_period:].fillna(method="ffill"),
"init_cash": initial_cash,
"commission": commission_percentage,
"allow_short": short_sell,
"short_max": margin_percentage,
"return_history": True,
"verbose": 0,
"plot": False
}
strategies = {
"smac": {
"fast_period": 15,
"slow_period": 40,
},
"emac": {
"fast_period": 15,
"slow_period": 40,
},
"buynhold": {},
"bbands": {
"period": 30,
"devfactor": 1,
},
"rsi": {
"rsi_period": 7,
"rsi_upper": 70,
"rsi_lower": 35,
},
"macd": {
"fast_period": 15,
"slow_period": 40,
"signal_period": 20,
"sma_period": 30,
"dir_period": 10,
},
}
strategies["multi"] = {
"strats": {strat: strategies[strat]
for strat in ("rsi", "smac")}
}
max_pnl = 0
max_strategy = None
max_res = None
max_trades = None
for strategy, s_kwargs in strategies.items():
try:
res, trades = backtest(strategy, **s_kwargs, **kwargs)
net_pnl = res.pnl[0]
if max_pnl < net_pnl:
max_pnl = net_pnl
max_res = res
max_trades = trades
max_strategy = strategy
except Exception:
pass
if max_strategy:
final_trades = [{
"type": order['type'],
"size": abs(order['size']),
"price": order['price'],
"date": order['dt'].to_pydatetime().date(),
"value": order.value,
"commission": order.commission,
"pnl": order.pnl
} for _, order in max_trades["orders"].iterrows()]
final_value = max_res.final_value[0]
final_capital = final_value - max_pnl
return {
"name": max_strategy,
"pnl": max_pnl,
"value": final_value,
"capital": final_capital,
"pnl_pct": (final_value - final_capital) / final_capital * 100,
"trades": final_trades
}
except Exception as e:
logger.exception(e)
return
from fastquant import backtest, get_stock_data
df = get_stock_data("AAAA", "2018-01-01", "2021-04-01")
res,plot = backtest('macd', df, init_cash=2000, fast_period=12, slow_period=26, signal_period=9, sma_period=30, dir_period=10, return_plot=True)
#plot.savefig('AAAA_macd.png')
from fastquant import get_stock_data, backtest
from datetime import date
today = date.today()
df = get_stock_data("WFG.TO", "2015-09-01", today)
backtest('smac', df, fast_period=25, slow_period=52)
# res = backtest("smac", df, fast_period=range(10, 30, 3), slow_period=range(40, 55, 3), verbose=False)
# print(res[['fast_period', 'slow_period', 'final_value']].head())
#
# "custom_column": "custom2"
# },
}
strategies["multi"] = {
"strats": {strat: strategies[strat]
for strat in ("rsi", "smac")}
}
max_pnl = 0
max_strategy = None
max_res = None
for strategy, s_kwargs in strategies.items():
try:
res = backtest(strategy, **s_kwargs, **kwargs)
net_pnl = res[0].pnl[0]
print(strategy)
print(f"Net PnL: {net_pnl}")
print()
if max_pnl < net_pnl:
max_pnl = net_pnl
max_res = res
max_strategy = strategy
except Exception:
pass
print()
from fastquant import backtest, get_stock_data
jfc = get_stock_data("JFC", "2018-01-01", "2019-01-01")
backtest('smac', jfc, fast_period=15, slow_period=40)
def back_test(i):
subset = stocks_data[stocks_data["Symbol"]==i]
#[HorizonPeriod:]
#converts date to datetime
stock = StockDataFrame.retype(subset[["Date","Open", "High", "Low", "Close", "Adj Close", "Volume"]])
#EVWMA
Short_EVWMA = pd.DataFrame(TA.EVWMA(subset,9))
Signal_EVWMA = pd.DataFrame(TA.EVWMA(subset,12))
Long_EVWMA = pd.DataFrame(TA.EVWMA(subset,26))
ATR = pd.DataFrame(TA.ATR(subset))
Short_EVWMA.columns = ['EVWMA_9']
Signal_EVWMA.columns = ['EVWMA_12']
Long_EVWMA.columns = ['EVWMA_26']
ATR.columns = ['ATR']
MACD_EVWMA = pd.DataFrame(Long_EVWMA['EVWMA_26'] - Short_EVWMA['EVWMA_9'])
MACD_EVWMA.columns = ['Signal']
#Adj Close
ts = subset[["Date","Adj Close"]]
ts.columns = ['ds', 'y']
#print(ts)
m = Prophet(daily_seasonality=True,yearly_seasonality=True)
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
m.add_seasonality(name='quarterly', period=91.25, fourier_order=7)
m.fit(ts[HorizonPeriod:])
#forecast = m.make_future_dataframe(periods=0, freq='D')
forecast = pd.DataFrame(idx3)
forecast.columns = ['ds']
#forecast2 = pd.DataFrame(df.index)[HorizonPeriodho+50:]
#forecast2.columns = ['ds']
# Predict and plot
pred = m.predict(forecast)
#pred2 = m.predict(forecast2)
dfpre = stock
idx1 = dfpre.index
#create entry for next trading day (i.e. idx3)
merged = idx1.union(idx3)
newdf = dfpre.reindex(merged)
#A. fbprophet return
expected_1day_return = pred.set_index("ds").yhat.pct_change().shift(-1).multiply(100)
newdf["custom"] = expected_1day_return.multiply(-1)
#B. no fbprophet
#newdf['custom'] = ts.set_index('ds')
#fbprophet
#newdf["custom"] = pred.set_index('ds')['yhat']
#rmse
#delta = len(pred.set_index('ds')['yhat'][HorizonPeriod:])-len(df['close'].dropna())
rmse = mean_squared_error(newdf['close'].dropna()[HoldoutPeriod:(HorizonPeriod-1)], pred.set_index('ds')['yhat'][HoldoutPeriod:(HorizonPeriod-1)], squared=True)
mape = MAPE(newdf['close'].dropna()[HoldoutPeriod:(HorizonPeriod-1)], pred.set_index('ds')['yhat'][HoldoutPeriod:(HorizonPeriod-1)])
#df = dfpre[(dfpre['Date']> "2018-01-01") & (df['Date']<= end)]
df = newdf[HorizonPeriod:]
#df["custom"] = (((pred.set_index('ds')['yhat']-ts.set_index('ds')['y'])/ts.set_index('ds')['y']).multiply(-1))[HorizonPeriod:]
with contextlib.redirect_stdout(None):
b = backtest("multi", df.dropna(), strats=strats_opt, return_history=True, buy_prop=0.10, sell_prop=1,commission=0.01, init_cash=1000)
r = {'backtest':b, 'score1':rmse, 'score2':mape, 'name':i, 'forecast':pred}
return (r)
#print(type(training_data))
#print(training_data.info())
#print(training_data.head())
model, _ = train_predictor.train(model, training_data, roondiwala.label_column_index)
preds, actual = train_predictor.predict(model, back_test_data, roondiwala.label_column_index)
preds = pd.DataFrame(data=np.array(preds), columns=['yhat'])
expected_1day_return = preds['yhat'].pct_change().shift(-1).multiply(100)
back_test_data['custom'] = expected_1day_return.multiply(-1).values
print('testing...')
res,history = backtest("custom",
back_test_data.dropna(),
upper_limit=3,
lower_limit=-2,
buy_prop=1,
sell_prop=1,
return_history=True,
execution_type='close',
plot=False,
verbose=0)
results[f'{ticker}, {start}-{end}'] = percentage_gain(float(res['init_cash']), float(res['final_value']))
dump = json.dumps(results)
output_file = open('roondiwala_fastquant_results.json', 'w')
output_file.write(dump)
output_file.close()
from fastquant import backtest, get_stock_data
df = get_stock_data("AAAA", "2018-01-01", "2021-04-01")
res, plot = backtest('bbands',
df,
init_cash=2000,
period=20,
devfactor=2.0,
return_plot=True)
#plot.savefig('AAAA_bbands.png')
