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@jit(nopython=True)
def simple_backtest_opposite(sig, item_id_changed, signal_times, bidprice, askprice,
loss_thres, serial_incr, timelimit, quantity_risk, risk, reward, initial_nav, quantity_maxrisk, ls, minq = 5, fixed_quantity = False):
"""
Performs a simple financial backtesting simulation based on given trading signals and prices,
adjusting positions according to specified trading rules, loss thresholds, and profit targets.
Parameters:
sig (numpy.ndarray): Array of trading signals where 1 indicates a long signal and -1 indicates a short signal.
item_id_changed (numpy.ndarray): Array indicating whether the item ID has changed, triggering a closeout of positions.
signal_times (numpy.ndarray): Array of timestamps for each trading signal.
bidprice (numpy.ndarray): Array of bid prices for the trading instrument.
askprice (numpy.ndarray): Array of ask prices for the trading instrument.
loss_thres (float): Threshold for loss at which positions should be closed to stop further loss.
serial_incr (int): Increment value for the serial number used in tracking trades.
timelimit (int): Time limit after which positions should be closed if no other conditions are met.
stop_loss (float): Stop loss level at which a position should be closed to limit losses.
profit_target (float): Target profit level at which a position should be closed to realize gains.
initial_quantity (int): Initial quantity of the instrument to be traded on receiving a trading signal.
Returns:
tuple: Contains multiple arrays including:
- signal (numpy.ndarray): Updated signals after processing.
- sig (numpy.ndarray): Original signals received.
- quantity (numpy.ndarray): Array showing the quantity of instrument held over time.
- tradeprice_leg1 (numpy.ndarray): Trading price for the first leg.
- case (numpy.ndarray): Array of integers indicating the case scenario applied at each step.
- return_since_signal (numpy.ndarray): Returns calculated since the last signal.
- serial (numpy.ndarray): Serial numbers for trades.
- traded_price_leg1 (numpy.ndarray): Actual traded prices after including transaction costs.
- compounded_return_since_signal (numpy.ndarray): Compounded returns since the last trading signal.
- time_since_signal (numpy.ndarray): Time elapsed since the last trading signal.
- total_nav (numpy.ndarray): Net asset value over time.
- crystallised_return (numpy.ndarray): Realized returns over the trading period.
- short_signal_on_array (numpy.ndarray): Array indicating periods with active short positions.
- long_signal_on_array (numpy.ndarray): Array indicating periods with active long positions.
"""
time_limit = timelimit
loss_threshold = loss_thres
serial_incr = serial_incr
lotsize1 = ls
txn_cost = 0.0 # 833
case = np.empty(bidprice.shape)
signal = np.empty(bidprice.shape)
short_signal_on_array = np.empty(bidprice.shape)
long_signal_on_array = np.empty(bidprice.shape)
quantity = np.empty(bidprice.shape)
maxquantity = np.empty(bidprice.shape)
serial = np.empty(bidprice.shape)
q1 = np.empty(bidprice.shape)
sl = np.empty(bidprice.shape)
tp = np.empty(bidprice.shape)
signal_price_leg1 = np.empty(bidprice.shape)
tradeprice_leg1 = np.empty(bidprice.shape)
traded_price_leg1 = np.empty(bidprice.shape)
time_since_signal = np.empty(bidprice.shape)
return_since_signal = np.empty(bidprice.shape)
compounded_return_since_signal = np.empty(bidprice.shape)
crystallised_quantity = np.empty(bidprice.shape)
crystallised_return = np.empty(bidprice.shape)
total_nav = np.empty(bidprice.shape)
traded_nav1 = np.empty(bidprice.shape)
short_signal_on = False
long_signal_on = False
short_signal_on_array[0] = 0
long_signal_on_array[0] = 0
signal[0] = 0
quantity[0] = 0
maxquantity[0] = 0
q1[0]=0
tp[0] = 0
sl[0] = 0
time_since_signal[0] = 0
return_since_signal[0] = 0
compounded_return_since_signal[0] = 0
crystallised_quantity[0] = 0
crystallised_return[0] = 0
total_nav[0] = initial_nav
traded_nav1[0] = 0
if bidprice[0] != 0:
tradeprice_leg1[0] = bidprice[0]
signal_price_leg1[0] = bidprice[0]
traded_price_leg1[0] = bidprice[0]
else:
tradeprice_leg1[0] = 0
signal_price_leg1[0] = 0
traded_price_leg1[0] = 0
for i in range(1,len(bidprice)):
case1long = (sig[i] == 1 and long_signal_on==False and short_signal_on==False)
case2long = (sig[i] == 1 and long_signal_on==True and short_signal_on==False)
case1short = (sig[i] == -1 and long_signal_on==False and short_signal_on==False)
case2short = (sig[i] == -1 and long_signal_on==False and short_signal_on==True)
if long_signal_on:
casecloseloss = (bidprice[i] <= sl[i-1])
caseclosetp = (bidprice[i] >= tp[i-1])
elif short_signal_on:
casecloseloss = (askprice[i] >= sl[i-1])
caseclosetp = (askprice[i] <= tp[i-1])
closingbeforeexpiry = (item_id_changed[i] == 1)
if signal_times[i] == 1:
if case1long:
if quantity[i-1] == 0:
case[i] = 5
signal[i] = 1
long_signal_on = True
short_signal_on = False
serial[i] = 1
time_since_signal[i] = 1
signal_price_leg1[i] = askprice[i]
traded_price_leg1[i] = askprice[i] * (1 + txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
if fixed_quantity:
quantity[i] = minq
maxquantity[i] = minq
else:
quantity[i], maxquantity[i], tp[i], sl[i] = define_q_and_tp(1, askprice[i], quantity_risk, risk, reward, total_nav[i-1], quantity_maxrisk, minq)
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
traded_nav1[i] = total_nav[i-1]
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif quantity[i-1] > 0:
raise KeyError
if ((askprice[i] - traded_price_leg1[i-1])/traded_price_leg1[i-1]) < -loss_threshold:
signal_price_leg1[i] = askprice[i]
traded_price_leg1[i] = askprice[i] * (1 + txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
traded_nav1[i] = traded_nav1[i-1]
serial[i] = serial[i-1] + serial_incr
time_since_signal[i] = time_since_signal[i-1] + 1
q1[i] = 5
if abs(quantity[i-1]) >= maxquantity:
quantity[i] = quantity[i-1]
else:
quantity[i] = quantity[i-1] + q1[i]
case[i] = 6
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
short_signal_on = False
long_signal_on = True
signal[i] = 1
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
traded_nav1[i] = traded_nav1[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = askprice[i]
short_signal_on = False
long_signal_on = True
signal[i] = 1
case[i] = 7
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
traded_nav1[i] = traded_nav1[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = askprice[i]
short_signal_on = False
long_signal_on = True
signal[i] = 0
case[i] = 8
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif case2long:
if quantity[i-1] == 0:
raise KeyError
long_signal_on = True
short_signal_on = False
serial[i] = 1
time_since_signal[i] = 1
quantity[i] = initial_quantity
traded_nav1[i] = total_nav[i-1]
signal_price_leg1[i] = askprice[i]
traded_price_leg1[i] = askprice[i] * (1 + txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
signal[i] = 1
case[i] = 5
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1) # lotsize?
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif quantity[i-1] > 0:
if casecloseloss or caseclosetp:
case[i]=34
signal[i] = 0
short_signal_on = False
long_signal_on = False
serial[i] = 0
time_since_signal[i] = 0
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
traded_nav1[i] = traded_nav1[i - 1]
quantity[i] = 0
maxquantity[i] = 0
sl[i] = 0
tp[i] = 0
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif (((bidprice[i] - traded_price_leg1[i-1])/traded_price_leg1[i-1])) < -loss_threshold:
case[i] = 6
signal[i] = 1
short_signal_on = False
long_signal_on = True
signal_price_leg1[i] = askprice[i]
traded_price_leg1[i] = askprice[i] * (1 + txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
traded_nav1[i] = traded_nav1[i-1]
serial[i] = serial[i-1] + serial_incr
time_since_signal[i] = time_since_signal[i-1] + 1
q1[i] = minq
if abs(quantity[i-1]) >= maxquantity[i-1]:
quantity[i] = quantity[i-1]
else:
quantity[i] = quantity[i-1] + q1[i]
sl[i] = sl[i - 1]
tp[i] = tp[i - 1]
maxquantity[i] = maxquantity[i - 1]
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
traded_nav1[i] = traded_nav1[i-1]
sl[i] = sl[i-1]
tp[i] = tp[i-1]
maxquantity[i] =maxquantity[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = bidprice[i]
short_signal_on = False
long_signal_on = True
signal[i] = 1
case[i] = 7
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
traded_nav1[i] = traded_nav1[i-1]
quantity[i] = quantity[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = askprice[i]
short_signal_on = False
long_signal_on = True
signal[i] = 0
case[i] = 8
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif case1short:
if quantity[i-1] == 0:
temp_signal[i] = 0
serial[i] = 1
time_since_signal[i] = 1
q1[i] = 5
traded_nav1[i] = total_nav[i-1]
signal_price_leg1[i] = bidprice[i]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = True
long_signal_on = False
signal[i] = -1
if fixed_quantity:
quantity[i] = -5
maxquantity[i] = -minq
else:
quantity[i], maxquantity[i], tp[i], sl[i] = define_q_and_tp(-1, bidprice[i], quantity_risk, risk, reward, total_nav[i-1], quantity_maxrisk, minq)
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
case[i] = 13
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif quantity[i-1] < 0:
raise KeyError
if (((bidprice[i] - traded_price_leg1[i-1])/traded_price_leg1[i-1])*-1) < -loss_threshold:
signal_price_leg1[i] = bidprice[i]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
temp_signal[i] = 0
traded_nav1[i] = traded_nav1[i-1]
serial[i] = serial[i-1] + serial_incr
time_since_signal[i] = time_since_signal[i-1] + 1
q1[i] = -5
if abs(quantity[i-1]) >= maxquantity:
quantity[i] = quantity[i-1]
else:
quantity[i] = quantity[i-1] + q1[i]
case[i] = 14
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
short_signal_on = True
long_signal_on = False
signal[i] = -1
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
temp_signal[i] = 0
traded_nav1[i] = traded_nav1[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = bidprice[i]
short_signal_on = True
long_signal_on = False
signal[i] = -1
case[i] = 15
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
temp_signal[i] = -1
traded_nav1[i] = traded_nav1[i-1]
signal_price_leg1[i] = 0
traded_price_leg1[i] = 0
tradeprice_leg1[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 16
return_since_signal[i] = 0
crystallised_return[i] = 0
compounded_return_since_signal[i] = 0
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif case2short:
if quantity[i-1] == 0:
raise KeyError
temp_signal[i] = 0
serial[i] = 1
time_since_signal[i] = 1
q1[i] = 5
quantity[i] = initial_quantity * -1
traded_nav1[i] = total_nav[i-1]
signal_price_leg1[i] = bidprice[i]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = True
long_signal_on = False
signal[i] = -1
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
case[i] = 13
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif quantity[i-1] < 0:
if casecloseloss or caseclosetp:
serial[i] = 0
time_since_signal[i] = 0
temp_signal[i] = 0
traded_nav1[i] = traded_nav1[i-1]
quantity[i] = 0
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = askprice[i]*(1+txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 38
return_since_signal[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif (((askprice[i] - traded_price_leg1[i-1])/traded_price_leg1[i-1])*-1) < -loss_threshold:
signal_price_leg1[i] = bidprice[i]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
temp_signal[i] = 0
traded_nav1[i] = traded_nav1[i-1]
tp[i],
serial[i] = serial[i-1] + serial_incr
time_since_signal[i] = time_since_signal[i-1] + 1
q1[i] = -5
if abs(quantity[i-1]) >= maxquantity:
quantity[i] = quantity[i-1]
else:
quantity[i] = quantity[i-1] + q1[i]
case[i] = 14
sl[i] = sl[i-1]
tp[i] = tp[i-1]
maxquantity[i] =maxquantity[i-1]
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
short_signal_on = True
long_signal_on = False
signal[i] = -1
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
temp_signal[i] = 0
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = askprice[i]
short_signal_on = True
long_signal_on = False
signal[i] = -1
traded_nav1[i] = traded_nav1[i-1]
sl[i] = sl[i-1]
tp[i] = tp[i-1]
maxquantity[i] =maxquantity[i-1]
case[i] = 15
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
temp_signal[i] = -1
traded_nav1[i] = traded_nav1[i-1]
signal_price_leg1[i] = 0
traded_price_leg1[i] = 0
tradeprice_leg1[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 16
return_since_signal[i] = 0
crystallised_return[i] = 0
compounded_return_since_signal[i] = 0
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
if long_signal_on:
if sig[i] == -1:
case[i] = 13
signal[i] = -1
short_signal_on = True
long_signal_on = False
serial[i] = 1
time_since_signal[i] = 1
# q1[i] = -minq
signal_price_leg1[i] = bidprice[i]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
crystallised_return[i] = (quantity[i - 1] * ((traded_price_leg1[i] - traded_price_leg1[i - 1])) * lotsize1)
traded_nav1[i] = total_nav[i-1] + crystallised_return[i]
if fixed_quantity:
quantity[i] = -minq
maxquantity[i] = -minq
else:
quantity[i], maxquantity[i], tp[i], sl[i] = define_q_and_tp(-1, bidprice[i], quantity_risk, risk, reward, traded_nav1[i], quantity_maxrisk, minq)
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif casecloseloss or caseclosetp:
raise KeyError
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
traded_nav1[i] = traded_nav1[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 26
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
traded_nav1[i] = traded_nav1[i-1]
quantity[i] = quantity[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = askprice[i]
short_signal_on = False
long_signal_on = True
signal[i] = 1
case[i] = 27
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif short_signal_on:
if sig[i] == 1:
case[i] = 30
signal[i] = 1
long_signal_on = True
short_signal_on = False
serial[i] = 1
time_since_signal[i] = 1
signal_price_leg1[i] = askprice[i]
traded_price_leg1[i] = askprice[i] * (1 + txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
crystallised_return[i] = (quantity[i - 1] * ((traded_price_leg1[i] - traded_price_leg1[i - 1])) * lotsize1)
traded_nav1[i] = total_nav[i-1] + crystallised_return[i]
if fixed_quantity:
quantity[i] = minq
maxquantity[i] = minq
else:
quantity[i], maxquantity[i], tp[i], sl[i] = define_q_and_tp(1, askprice[i], quantity_risk, risk, reward, traded_nav1[i], quantity_maxrisk, minq)
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif casecloseloss or caseclosetp:
raise KeyError
serial[i] = 0
time_since_signal[i] = 0
temp_signal[i] = 0
traded_nav1[i] = traded_nav1[i-1]
quantity[i] = 0
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = askprice[i]*(1+txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 29
return_since_signal[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
traded_nav1[i] = traded_nav1[i-1]
quantity[i] = quantity[i-1]
temp_signal[i] = temp_signal[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = bidprice[i]
short_signal_on = True
long_signal_on = False
signal[i] = -1
case[i] = 30
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
raise KeyError
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
traded_nav1[i] = traded_nav1[i-1]
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
signal_price_leg1[i] = 0
traded_price_leg1[i] = 0
tradeprice_leg1[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 31
return_since_signal[i] = 0
crystallised_return[i] = 0
compounded_return_since_signal[i] = 0
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
if (time_since_signal[i] >= time_limit ) or closingbeforeexpiry:
if long_signal_on:
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
signal_price_leg1[i] = signal_price_leg1[i-1]
tradeprice_leg1[i] = traded_price_leg1[i]
case[i] = 32
serial[i] = 0
time_since_signal[i] = 0
temp_signal[i] = 0
traded_nav1[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] = 0
quantity[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif short_signal_on:
traded_price_leg1[i] = askprice[i]*(1+txn_cost)
signal_price_leg1[i] = signal_price_leg1[i-1]
temp_signal[i] = 0
tradeprice_leg1[i] = traded_price_leg1[i]
traded_nav1[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
case[i] = 33
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
traded_price_leg1[i] = 0
signal_price_leg1[i] = 0
temp_signal[i] = 0
traded_nav1[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] = 0
tradeprice_leg1[i] = 0
crystallised_return[i] = 0
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
return_since_signal[i] = 0
compounded_return_since_signal[i] = 0
case[i] = 34
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif signal_times[i] == 0:
if long_signal_on:
if casecloseloss or caseclosetp:
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
temp_signal[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 26
traded_nav1[i] = traded_nav1[i-1]
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
temp_signal[i] = temp_signal[i-1]
traded_nav1[i] = traded_nav1[i-1]
sl[i] = sl[i-1]
tp[i] = tp[i-1]
maxquantity[i] = maxquantity[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = bidprice[i]
short_signal_on = False
long_signal_on = True
signal[i] = 1
case[i] = 27
return_since_signal[i] = (quantity[i]*((bidprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif short_signal_on:
if casecloseloss or caseclosetp:
serial[i] = 0
time_since_signal[i] = 0
temp_signal[i] = 0
traded_nav1[i] = traded_nav1[i-1]
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
quantity[i] = 0
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = askprice[i]*(1+txn_cost)
tradeprice_leg1[i] = traded_price_leg1[i]
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 29
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = serial[i-1]
time_since_signal[i] = time_since_signal[i-1] + 1
quantity[i] = quantity[i-1]
temp_signal[i] = temp_signal[i-1]
traded_nav1[i] = total_nav[i-1]
sl[i] = sl[i-1]
tp[i] = tp[i-1]
maxquantity[i] =maxquantity[i-1]
signal_price_leg1[i] = signal_price_leg1[i-1]
traded_price_leg1[i] = traded_price_leg1[i-1]
tradeprice_leg1[i] = askprice[i]
short_signal_on = True
long_signal_on = False
signal[i] = -1
case[i] = 30
return_since_signal[i] = (quantity[i]*((askprice[i] - traded_price_leg1[i]))*lotsize1)
crystallised_return[i] = 0
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
serial[i] = 0
time_since_signal[i] = 0
quantity[i] = 0
temp_signal[i] = 0
traded_nav1[i] = total_nav[i-1]
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
signal_price_leg1[i] = 0
traded_price_leg1[i] = 0
tradeprice_leg1[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
case[i] = 31
return_since_signal[i] = 0
crystallised_return[i] = 0
compounded_return_since_signal[i] = 0
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
if (time_since_signal[i] >= time_limit) or closingbeforeexpiry:
if long_signal_on:
traded_price_leg1[i] = bidprice[i]*(1-txn_cost)
signal_price_leg1[i] = signal_price_leg1[i-1]
tradeprice_leg1[i] = traded_price_leg1[i]
serial[i] = 0
time_since_signal[i] = 0
traded_nav1[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
quantity[i] = 0
temp_signal[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
case[i] = 60
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
elif short_signal_on:
traded_price_leg1[i] = askprice[i]*(1+txn_cost)
signal_price_leg1[i] = signal_price_leg1[i-1]
tradeprice_leg1[i] = traded_price_leg1[i]
serial[i] = 0
time_since_signal[i] = 0
traded_nav1[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
quantity[i] = 0
temp_signal[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
return_since_signal[i] = 0
crystallised_return[i] = (quantity[i-1]*((traded_price_leg1[i] - traded_price_leg1[i-1]))*lotsize1)
compounded_return_since_signal[i] = crystallised_return[i] + return_since_signal[i]
case[i] = 61
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
else:
traded_price_leg1[i] = 0
signal_price_leg1[i] = 0
tradeprice_leg1[i] = 0
crystallised_return[i] = 0
serial[i] = 0
time_since_signal[i] = 0
temp_signal[i] = 0
traded_nav1[i] = 0
sl[i] = 0
tp[i] = 0
maxquantity[i] =0
quantity[i] = 0
short_signal_on = False
long_signal_on = False
signal[i] = 0
return_since_signal[i] = 0
compounded_return_since_signal[i] = 0
case[i] = 62
total_nav[i] = total_nav[i-1] + crystallised_return[i] + return_since_signal[i] - return_since_signal[i-1]
short_signal_on_array[i] = np.where(short_signal_on == False, 0, 1)
long_signal_on_array[i] = np.where(long_signal_on == False, 0, 1)
if total_nav[i] <= 0:
break
return signal, sig, quantity, maxquantity, tradeprice_leg1, case, return_since_signal,serial, traded_price_leg1, compounded_return_since_signal, time_since_signal, total_nav, crystallised_return, short_signal_on_array, long_signal_on_array
@jit(nopython=True)
def define_q_and_tp(buy_sell, closeprice, quantity_risk, risk, reward, initial_nav, quantity_maxrisk, min_q):
q = (initial_nav * quantity_risk) / closeprice
maxquantity = (initial_nav * quantity_maxrisk) / closeprice
q = (q // min_q) * min_q
maxquantity = (maxquantity // min_q) * min_q
if q < min_q:
q = min_q
if maxquantity < min_q:
maxquantity = min_q
q = q * buy_sell
maxquantity = maxquantity * buy_sell
tp = closeprice * (1 + buy_sell * reward)
sl = closeprice * (1 - buy_sell * risk)
return float(q), float(maxquantity), float(tp), float(sl)Editor is loading...
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