def myTradingSystem(
DATE, OPEN, HIGH, LOW, CLOSE, VOL, USA_ADP, USA_EARN, USA_HRS, USA_BOT,
USA_BC, USA_BI, USA_CU, USA_CF, USA_CHJC, USA_CFNAI, USA_CP, USA_CCR,
USA_CPI, USA_CCPI, USA_CINF, USA_DFMI, USA_DUR, USA_DURET, USA_EXPX,
USA_EXVOL, USA_FRET, USA_FBI, USA_GBVL, USA_GPAY, USA_HI, USA_IMPX,
USA_IMVOL, USA_IP, USA_IPMOM, USA_CPIC, USA_CPICM, USA_JBO, USA_LFPR,
USA_LEI, USA_MPAY, USA_MP, USA_NAHB, USA_NLTTF, USA_NFIB, USA_NFP,
USA_NMPMI, USA_NPP, USA_EMPST, USA_PHS, USA_PFED, USA_PP, USA_PPIC,
USA_RSM, USA_RSY, USA_RSEA, USA_RFMI, USA_TVS, USA_UNR, USA_WINV,
exposure, equity, settings):
nMarkets = CLOSE.shape[1]
lookback = settings['lookback']
pos = np.zeros(nMarkets)
markets = settings['markets']
w = settings['market_factor_weights']
lweights, sweights = econ_long_short_allocation(
markets,
DATE[0],
DATE[-1],
w,
activate=settings['dynamic_portfolio_allocation'])
sentiment_data = settings['sentiment_data']
covid_data = settings['covid_data']
# to understand how this system works
print("Using data from {} onwards to predict/take position in {}".format(
DATE[0], DATE[-1]))
OPEN = np.transpose(OPEN)[1:]
HIGH = np.transpose(HIGH)[1:]
LOW = np.transpose(LOW)[1:]
CLOSE = np.transpose(CLOSE)[1:]
VOL = np.transpose(VOL)[1:]
if settings['model'] == 'TA':
'''
Based on factors from https://www.investing.com/technical/us-spx-500-futures-technical-analysis
################ TREND FOLOWING ################
Simple Moving Average (SMA) crosses, period 5,10,20,50,100,200
'''
# indicators
SMA5s = [SMA(close, 5) for close in CLOSE]
SMA10s = [SMA(close, 10) for close in CLOSE]
SMA20s = [SMA(close, 20) for close in CLOSE]
SMA50s = [SMA(close, 50) for close in CLOSE]
SMA100s = [SMA(close, 100) for close in CLOSE]
SMA200s = [SMA(close, 200) for close in CLOSE]
# signals
def buy_condition(close, sma):
return (close[-1] > sma[-1]) and (close[-2] <= sma[-2])
def sell_condition(close, sma):
return (close[-1] < sma[-1]) and (close[-2] >= sma[-2])
SMA5_cross_buys = [
True if buy_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA5s)
]
SMA5_cross_sells = [
True if sell_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA5s)
]
SMA10_cross_buys = [
True if buy_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA10s)
]
SMA10_cross_sells = [
True if sell_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA10s)
]
SMA20_cross_buys = [
True if buy_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA20s)
]
SMA20_cross_sells = [
True if sell_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA20s)
]
SMA50_cross_buys = [
True if buy_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA50s)
]
SMA50_cross_sells = [
True if sell_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA50s)
]
SMA100_cross_buys = [
True if buy_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA100s)
]
SMA100_cross_sells = [
True if sell_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA100s)
]
SMA200_cross_buys = [
True if buy_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA200s)
]
SMA200_cross_sells = [
True if sell_condition(close, sma) else False
for close, sma in zip(CLOSE, SMA200s)
]
'''
Exponential Moving Average (EMA) crosses, period 5,10,20,50,100,200
'''
# indicators
EMA5s = [EMA(close, 5) for close in CLOSE]
EMA10s = [EMA(close, 10) for close in CLOSE]
EMA20s = [EMA(close, 20) for close in CLOSE]
EMA50s = [EMA(close, 50) for close in CLOSE]
EMA100s = [EMA(close, 100) for close in CLOSE]
EMA200s = [EMA(close, 200) for close in CLOSE]
# signals
# def condition(close, ema):
# return (close[-1] > ema[-1]) and (close[-2] <= ema[-2])
def buy_condition(close, ema):
return (close[-1] > ema[-1]) and (close[-2] <= ema[-2])
def sell_condition(close, ema):
return (close[-1] < ema[-1]) and (close[-2] >= ema[-2])
EMA5_cross_buys = [
True if buy_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA5s)
]
EMA5_cross_sells = [
True if sell_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA5s)
]
EMA10_cross_buys = [
True if buy_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA10s)
]
EMA10_cross_sells = [
True if sell_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA10s)
]
EMA20_cross_buys = [
True if buy_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA20s)
]
EMA20_cross_sells = [
True if sell_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA20s)
]
EMA50_cross_buys = [
True if buy_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA50s)
]
EMA50_cross_sells = [
True if sell_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA50s)
]
EMA100_cross_buys = [
True if buy_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA100s)
]
EMA100_cross_sells = [
True if sell_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA100s)
]
EMA200_cross_buys = [
True if buy_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA200s)
]
EMA200_cross_sells = [
True if sell_condition(close, ema) else False
for close, ema in zip(CLOSE, EMA200s)
]
'''
Average Directional Movement Index (ADX), period 14
'''
# indicators
ADXs = [
ADX(high, low, close, 14)
for high, low, close in zip(HIGH, LOW, CLOSE)
]
# signals
# adx[0] is mDI, adx[1] is pDI, adx[2] is actual ADX
def bullish_condition(adx):
# Bullish strong trend cross
return (adx[2][-1] > 20) and (adx[1][-1] > adx[0][-1]) and (
adx[1][-2] <= adx[0][-2])
def bearish_condition(adx):
# Bearish strong trend cross
return (adx[2][-1] > 20) and (adx[1][-1] < adx[0][-1]) and (
adx[1][-2] >= adx[0][-2])
ADX_bullish_crosses = [
True if bullish_condition(adx) else False for adx in ADXs
]
ADX_bearish_crosses = [
True if bearish_condition(adx) else False for adx in ADXs
]
'''
Moving Average Convergence Divergence (MACD) fast=12, slow=26
'''
# indicator
EMA12s = [EMA(close, 12) for close in CLOSE]
EMA26s = [EMA(close, 26) for close in CLOSE]
MACDs = [[(a - b) if b is not None else None
for a, b in zip(EMA12, EMA26)]
for EMA12, EMA26 in zip(EMA12s, EMA26s)]
# signals
def bullish_condition(MACD):
# Bullish zero cross which sustains for 2 days (reduce false signals)
return (MACD[-3] <= 0) and (MACD[-2] > 0) and (MACD[-1] > 0)
def bearish_condition(MACD):
# Bearish zero cross
return (MACD[-3] >= 0) and (MACD[-2] < 0) and (MACD[-1] < 0)
MACD_bullish_zero_cross = [
True if bullish_condition(MACD) else False for MACD in MACDs
]
MACD_bearish_zero_cross = [
True if bearish_condition(MACD) else False for MACD in MACDs
]
'''
Commodity Channel Index (CCI), period 14
'''
# indicator
CCIs = [
CCI(high, low, close, 14)
for high, low, close in zip(HIGH, LOW, CLOSE)
]
# signals
def bullish_condition(CCI):
return (CCI[-1] > 100) and (CCI[-2] <= 100)
def bearish_condition(CCI):
return (CCI[-1] < -100) and (CCI[-2] >= -100)
CCI_emerging_bulls = [
True if bullish_condition(CCI) else False for CCI in CCIs
]
CCI_emerging_bears = [
True if bearish_condition(CCI) else False for CCI in CCIs
]
'''
################ MOMENTUM ################
Relative Strength Index (RSI), period 14
'''
# indicator
RSIs = [RSI(close) for close in CLOSE]
# signals
def bullish_reversal(rsi, sma200, close):
# Uptrend and cross 30 to become oversold (Bullish)
return (close[-1] > sma200[-1]) and (rsi[-2] >= 30) and (rsi[-1] <
30)
def bearish_reversal(rsi, sma200, close):
# Downtrend and cross 70 to become overbought (Bearish)
return (close[-1] < sma200[-1]) and (rsi[-2] <= 70) and (rsi[-1] >
70)
def underbought_uptrend(rsi, sma200, close):
# Uptrend and underbought
return (close[-1] > sma200[-1]) and (rsi[-1] < 50)
def undersold_downtrend(rsi, sma200, close):
# Downtrend and undersold
return (close[-1] < sma200[-1]) and (rsi[-1] > 50)
RSI_bullish_reversal = [
True if bullish_reversal(rsi, sma200, close) else False
for rsi, sma200, close in zip(RSIs, SMA200s, CLOSE)
]
RSI_bearish_reversal = [
True if bearish_reversal(rsi, sma200, close) else False
for rsi, sma200, close in zip(RSIs, SMA200s, CLOSE)
]
RSI_underbought_uptrend = [
True if underbought_uptrend(rsi, sma200, close) else False
for rsi, sma200, close in zip(RSIs, SMA200s, CLOSE)
]
RSI_undersold_downtrend = [
True if undersold_downtrend(rsi, sma200, close) else False
for rsi, sma200, close in zip(RSIs, SMA200s, CLOSE)
]
'''
Stochastic Oscillator, fast 14, slow 3
'''
# indicators
StochOscs = [
StochOsc(close, high, low, 14, 3)
for close, high, low in zip(CLOSE, HIGH, LOW)
]
# signals
# stochosc[0] is Ks, stochosc[1] is Ds
def bullish_cross(stochosc):
# K (fast) cross D (slow) from below
return (stochosc[0][-2] <= stochosc[1][-2]) and (stochosc[0][-1] >
stochosc[1][-1])
def bearish_cross(stochosc):
# K (fast) cross D (slow) from above
return (stochosc[0][-2] >= stochosc[1][-2]) and (stochosc[0][-1] <
stochosc[1][-1])
StochOsc_bullish_cross = [
True if bullish_cross(stochosc) else False
for stochosc in StochOscs
]
StochOsc_bearish_cross = [
True if bearish_cross(stochosc) else False
for stochosc in StochOscs
]
'''
Williams %R, 14 period
'''
# indicator
WilliamsRs = [
WilliamsR(high, low, close)
for high, low, close in zip(HIGH, LOW, CLOSE)
]
# signals
def bullish(wr, close, sma100):
# Overbought price action
return (wr[-1] > -20) and (close[-1] > sma100[-1]) and (
close[-2] <= sma100[-2])
def bearish(wr, close, sma100):
# Oversold price action
return (wr[-1] < -80) and (close[-1] < sma100[-1]) and (
close[-2] >= sma100[-2])
WilliamsR_uptrend = [
True if bullish(wr, close, sma100) else False
for wr, close, sma100 in zip(WilliamsRs, CLOSE, SMA100s)
]
WilliamsR_downtrend = [
True if bearish(wr, close, sma100) else False
for wr, close, sma100 in zip(WilliamsRs, CLOSE, SMA100s)
]
'''
Ultimate Oscillator, periods 20,40,80
'''
# indicator
UltiOscs = [
UltiOsc(high, low, close, 20, 40, 80)
for high, low, close in zip(HIGH, LOW, CLOSE)
]
# signals
def bullish_cross(ultiosc):
# Bullish center cross
return (ultiosc[-1] > 50) and (ultiosc[-2] <= 50)
def bearish_cross(ultiosc):
# Bearish center cross
return (ultiosc[-1] < 50) and (ultiosc[-2] >= 50)
def bullish_reversal(ultiosc):
# Bullish reversal from oversold
return (ultiosc[-1] < 30) and (ultiosc[-2] >= 30)
def bearish_reversal(ultiosc):
# Bearish reversal from overbought
return (ultiosc[-1] > 70) and (ultiosc[-2] >= 70)
UltiOsc_bullish_cross = [
True if bullish_cross(ultiosc) else False for ultiosc in UltiOscs
]
UltiOsc_bearish_cross = [
True if bearish_cross(ultiosc) else False for ultiosc in UltiOscs
]
UltiOsc_bullish_reversal = [
True if bullish_reversal(ultiosc) else False
for ultiosc in UltiOscs
]
UltiOsc_bearish_reversal = [
True if bearish_reversal(ultiosc) else False
for ultiosc in UltiOscs
]
'''
################ VOLUME ################
Accumulation / Distribution Index (ADI)
'''
# indicator
ADIs = [
ADI(high, low, close, vol)
for high, low, close, vol in zip(HIGH, LOW, CLOSE, VOL)
]
def bullish_trend(close, adi, sma200):
# bullish trend confirmation
return (close[-1] > sma200[-1]) and (close[-2] <= sma200[-2]) and (
adi[-1] > adi[-2])
def bearish_trend(close, adi, sma200):
# bearish trend confirmation
return (close[-1] < sma200[-1]) and (close[-2] >= sma200[-2]) and (
adi[-1] < adi[-2])
ADI_bullish_trend_confo = [
True if bullish_trend(close, adi, sma200) else False
for close, adi, sma200 in zip(CLOSE, ADIs, SMA200s)
]
ADI_bearish_trend_confo = [
True if bearish_trend(close, adi, sma200) else False
for close, adi, sma200 in zip(CLOSE, ADIs, SMA200s)
]
'''
On-Balance Volume (OBV)
'''
# indicator
OBVs = [OBV(close, vol) for close, vol in zip(CLOSE, VOL)]
# signals
def bullish_trend(obv):
return (obv[-1] > obv[-2]) and (obv[-2] > obv[-3])
def bearish_trend(obv):
return (obv[-1] < obv[-2]) and (obv[-2] < obv[-3])
OBV_bullish_trend_confo = [
True if bullish_trend(obv) else False for obv in OBVs
]
OBV_bearish_trend_confo = [
True if bearish_trend(obv) else False for obv in OBVs
]
'''
################ VOLATILITY ################
Bollinger Bands (BB), 20 period
'''
# indicator + signal
BBs = [BB(close, 20) for close in CLOSE]
BB_bullish_reversal = [True if bb[1][-1] == 1 else False for bb in BBs]
BB_bearish_reversal = [True if bb[0][-1] == 1 else False for bb in BBs]
'''
Execution
'''
for i in range(0, nMarkets - 1):
future_name = markets[i + 1]
# # Trend following
# if (SMA5_cross_buys[i] == True) or (SMA10_cross_buys[i] == True) or (SMA20_cross_buys[i] == True) or (SMA50_cross_buys[i] == True) or (SMA100_cross_buys[i] == True) or (SMA200_cross_buys == True):
# pos[i+1] = 1
# if (SMA5_cross_sells[i] == True) or (SMA10_cross_sells[i] == True) or (SMA20_cross_sells[i] == True) or (SMA50_cross_sells[i] == True) or (SMA100_cross_sells[i] == True) or (SMA200_cross_sells == True):
# pos[i+1] = -1
# Mean reverting
# if (SMA5_cross_buys[i] == True) or (SMA10_cross_buys[i] == True) or (SMA20_cross_buys[i] == True) or (SMA50_cross_buys[i] == True) or (SMA100_cross_buys[i] == True) or (SMA200_cross_buys == True):
# pos[i+1] = -1
# if (SMA5_cross_sells[i] == True) or (SMA10_cross_sells[i] == True) or (SMA20_cross_sells[i] == True) or (SMA50_cross_sells[i] == True) or (SMA100_cross_sells[i] == True) or (SMA200_cross_sells == True):
# pos[i+1] = 1
# # Trend following
# if (EMA5_cross_buys[i] == True) or (EMA10_cross_buys[i] == True) or (EMA20_cross_buys[i] == True) or (EMA50_cross_buys[i] == True) or (EMA100_cross_buys[i] == True) or (EMA200_cross_buys == True):
# pos[i+1] = 1
# if (EMA5_cross_sells[i] == True) or (EMA10_cross_sells[i] == True) or (EMA20_cross_sells[i] == True) or (EMA50_cross_sells[i] == True) or (EMA100_cross_sells[i] == True) or (EMA200_cross_sells == True):
# pos[i+1] = -1
# # Mean-reverting
# if (EMA5_cross_buys[i] == True) or (EMA10_cross_buys[i] == True) or (EMA20_cross_buys[i] == True) or (EMA50_cross_buys[i] == True) or (EMA100_cross_buys[i] == True) or (EMA200_cross_buys == True):
# pos[i+1] = -1
# if (EMA5_cross_sells[i] == True) or (EMA10_cross_sells[i] == True) or (EMA20_cross_sells[i] == True) or (EMA50_cross_sells[i] == True) or (EMA100_cross_sells[i] == True) or (EMA200_cross_sells == True):
# pos[i+1] = 1
# if ADX_bullish_crosses[i] == True:
# pos[i+1] = 1
# elif ADX_bearish_crosses[i] == True:
# pos[i+1] = -1
# if MACD_bullish_zero_cross[i] == True:
# pos[i+1] = 1
# elif MACD_bearish_zero_cross[i] == True:
# pos[i+1] = -1
# if CCI_emerging_bulls[i] == True:
# pos[i+1] = 1
# elif CCI_emerging_bears[i] == True:
# pos[i+1] = -1
# if RSI_bullish_reversal[i] == True:
# pos[i+1] = 1
# elif RSI_bearish_reversal[i] == True:
# pos[i+1] = -1
# if StochOsc_bullish_cross[i] == True:
# pos[i+1] = 1
# elif StochOsc_bearish_cross[i] == True:
# pos[i+1] = -1
# if WilliamsR_uptrend[i] == True:
# pos[i+1] = 1
# elif WilliamsR_downtrend[i] == True:
# pos[i+1] = -1
# if UltiOsc_bullish_cross[i] == True:
# pos[i+1] = 1
# elif UltiOsc_bearish_cross[i] == True:
# pos[i+1] = -1
# if UltiOsc_bullish_reversal[i] == True:
# pos[i+1] = 1
# elif UltiOsc_bearish_reversal[i] == True:
# pos[i+1] = -1
# if ADI_bullish_trend_confo[i] == True:
# pos[i+1] = lweights[future_name]
# elif ADI_bearish_trend_confo[i] == True:
# pos[i+1] = sweights[future_name]
# if OBV_bullish_trend_confo[i] == True:
# pos[i+1] = 1
# elif OBV_bearish_trend_confo[i] == True:
# pos[i+1] = -1
# # Mean-reverting
# if BB_bullish_reversal[i] == True:
# pos[i+1] = 1
# elif BB_bearish_reversal[i] == True:
# pos[i+1] = -1
# # Trend-following
if BB_bullish_reversal[i] == True:
pos[i + 1] = sweights[future_name]
elif BB_bearish_reversal[i] == True:
pos[i + 1] = lweights[future_name]
elif settings['model'] == 'LIGHTGBM':
for i in range(0, nMarkets - 1):
future_name = markets[i + 1]
if future_name in [
"CASH", "F_ED", "F_UZ", "F_SS", "F_ZQ", "F_EB", "F_VW",
"F_F"
]:
feature_ADI = ADI(HIGH[i], LOW[i], CLOSE[i], VOL[i])
feature_WilliamsR = WilliamsR(HIGH[i], LOW[i], CLOSE[i])
feature_BB_high_crosses, feature_BB_low_crosses = BB(
CLOSE[i], 10)
feature_CCI = CCI(LOW[i], CLOSE[i], VOL[i], 10)
features = np.array([[
OPEN[i][-1],
HIGH[i][-1],
LOW[i][-1],
CLOSE[i][-1],
VOL[i][-1],
feature_ADI[-1],
feature_WilliamsR[-1],
feature_BB_high_crosses[-1],
feature_BB_low_crosses[-1],
feature_CCI[-1],
CLOSE[i][-2],
CLOSE[i][-3],
]])
model_dir = f"./data/lgb_models/{markets[i+1]}_model"
prediction = get_lgb_prediction(model_dir, features)[0]
if prediction == 1:
pos[i + 1] = lweights[future_name]
elif prediction == -1:
pos[i + 1] = sweights[future_name]
elif settings['model'] == 'sentiment':
'''
How sentiment of tweets from Bloomberg/Trump affect VIX, Gold and Treasuries
'''
for i in range(0, nMarkets - 1):
future_name = markets[i + 1]
if future_name in ['F_GC']: #'F_VX','F_GC','F_TU'
sentiment = sentiment_data[future_name]
today = datetime.strptime(str(DATE[-1]), '%Y%m%d').date()
if (today - sentiment['DATE'].tolist()[0]
).days > 30: # at least 30 days for training
train = sentiment[sentiment['DATE'] < today]
test = sentiment[sentiment['DATE'] == today]
trainY = train['CLOSE']
del train['DATE'], train['CLOSE']
trainX = train
del test['DATE'], test['CLOSE']
model = RandomForestRegressor()
model.fit(trainX, trainY)
pred_CLOSE = model.predict(test)[0]
if pred_CLOSE > CLOSE[i][-2]:
pos[i + 1] = 1
else:
pos[i + 1] = -1
elif settings['model'] == 'covid':
'''
How no. of covid cases in each country affects their overall markets
'sharpe': 1.3048, 'sortino': 2.3477,
avg longs per day: 1.35 , avg shorts per day: 6.6
'''
for i in range(0, nMarkets - 1):
country = None
future_name = markets[i + 1]
if future_name in ['F_ES', 'F_MD', 'F_NQ', 'F_RU', 'F_XX', 'F_YM']:
country = 'US'
elif future_name in ['F_AX', 'F_DM', 'F_DZ']:
country = 'Germany'
elif future_name == 'F_CA':
country = 'France'
elif future_name == 'F_LX':
country = 'United Kingdom'
elif future_name == 'F_FP':
country = 'Finland'
elif future_name == 'F_NY':
country = 'Japan'
elif future_name == 'F_PQ':
country = 'Portugal'
elif future_name in ['F_SH', 'F_SX']:
country = 'Switzerland'
if country:
df = covid_data[covid_data['Country/Region'] == country].T.sum(
axis=1).reset_index()
df = df.iloc[1:]
df['index'] = df['index'].apply(lambda x: x + "20")
df['index'] = df['index'].apply(
lambda x: datetime.strptime(x, '%m/%d/%Y').date())
future = pd.DataFrame({'DATE': DATE, 'CLOSE': CLOSE[i]})
future['CLOSE'] = future['CLOSE'].shift(-1)
future['DATE'] = future['DATE'].apply(
lambda x: datetime.strptime(str(x), '%Y%m%d').date())
df = pd.merge(df, future, left_on='index', right_on='DATE')
df = df[df[0] != 0][[0, 'CLOSE']].rename(columns={0: "count"})
if len(df) > 10:
reg = LinearRegression().fit(
np.array(df['count'].values[:-1]).reshape(-1, 1),
df['CLOSE'].values[:-1])
pred_CLOSE = reg.predict(
np.array(df['count'].values[-1]).reshape(1, -1))[0]
if pred_CLOSE > CLOSE[i][-2]:
pos[i + 1] = 1
else:
pos[i + 1] = -1
elif settings['model'] == 'ARIMA':
for i in range(0, nMarkets - 1):
try:
if markets[i + 1] not in ARIMA_MODELS:
model = auto_arima(np.log(CLOSE[i][:-1]),
trace=False,
error_action='ignore',
suppress_warnings=True)
ARIMA_MODELS[markets[i + 1]] = model.fit(
np.log(CLOSE[i][:-1]))
model = ARIMA_MODELS[markets[i + 1]].fit(np.log(CLOSE[i][:-1]))
pred = model.predict(n_periods=1)[0]
# print(markets[i+1],pred, np.log(CLOSE[i][-1]))
pos[i + 1] = 1 if pred > np.log(CLOSE[i][-1]) else -1
except:
pos[i + 1] = 0
print(f"Today's position in the {len(markets)} futures: {pos}")
elif settings['model'] == 'GARCH':
# Log return of the closing data
#Prameters
bound1 = 1
bound2 = 1
cor_dir = f'./data/garch_models/correlation.txt'
with open(cor_dir) as f:
cor_dict = json.load(f)
log_return = np.diff(np.log(CLOSE))
#print(log_return)
#log_return = log_return[~np.isnan(log_return)]
#print(log_return[1])
for i in range(0, nMarkets - 1):
train_Xs = log_return[i][:-1]
#test_Xs = log_return[i][-1]
sd = np.var(train_Xs)
# define model
model_dir = f'./data/garch_models/{markets[i+1]}_garch_model.txt'
with open(model_dir) as f:
params_dict = json.load(f)
p = params_dict['order'][0]
q = params_dict['order'][1]
model = arch_model(train_Xs, p=p, q=q)
model_fixed = model.fix(params_dict['params'])
# forecast the test set
forecasts = model_fixed.forecast()
#expected = forecasts.mean.iloc[-1:]['h.1']
var = forecasts.variance.iloc[-1:]['h.1']
#print(type(variance))
if (cor_dict[markets[i + 1]] > 0.03):
if (float(np.sqrt(var)) > bound1 * np.std(train_Xs)):
pos[i] = 1
elif (float(np.sqrt(var)) < bound2 * np.std(train_Xs)):
pos[i] = -1
else:
pos[i] = 0
elif (cor_dict[markets[i + 1]] < -0.03):
if (float(np.sqrt(var)) > bound1 * np.std(train_Xs)):
pos[i] = -1
elif (float(np.sqrt(var)) < bound2 * np.std(train_Xs)):
pos[i] = 1
else:
pos[i] = 0
else:
pos[i] = 0
# With the estimated return and variance, we can apply portfolio optimization
#print((np.array(result) * np.array(truth)).sum() / len(result))
elif settings['model'] == 'fourier':
#Parameters that filter the signal with specific range of signals
#Note that the lower bound should be larger than 0 and upper bound smaller than 1
filter_type = 'customed'
my_frequency_bound = [0.05, 0.2]
filter_type = 'low' #Specify high/mid/low/customed for weekly signals, weekly to monthly signals, above monthly signals or customed frequency range
if filter_type == 'high':
frequency_bound = [0.2, 1]
elif filter_type == 'mid':
frequency_bound = [0.05, 0.2]
elif filter_type == 'low':
frequency_bound = [0, 0.05]
elif filter_type == 'customed':
frequency_bound = my_frequency_bound
#Transform the close data by fourier filtering, only signals within the specific range remains
transformed_close = []
for i in range(0, nMarkets - 1):
signal = CLOSE[i][:-1]
fft = np.fft.rfft(signal)
T = 1 # sampling interval
N = len(signal)
f = np.linspace(0, 1 / T, N)
fft_filtered = []
for j in range(int(N / 2)):
if f[j] > frequency_bound[0] and f[j] < frequency_bound[1]:
fft_filtered.append(fft[j])
else:
fft_filtered.append(0)
signal_filtered = np.fft.irfft(fft_filtered)
transformed_close.append(list(signal_filtered))
periodLonger = 200
periodShorter = 40
smaLongerPeriod = np.nansum(
np.array(transformed_close)[:,
-periodLonger:], axis=1) / periodLonger
smaShorterPeriod = np.nansum(
np.array(transformed_close)[:, -periodShorter:],
axis=1) / periodShorter
longEquity = smaShorterPeriod > smaLongerPeriod
shortEquity = ~longEquity
pos_1 = np.zeros(nMarkets - 1)
pos_1[longEquity] = 1
pos_1[shortEquity] = -1
pos = np.array([0] + list(pos_1))
elif settings['model'] == 'pearson':
'''
Pairwise correlation, taking position based on the greatest variation from
average of the past 50 periods of 50 days
'''
#'sharpe': 0.57939, 'sortino': 0.9027189, 'returnYearly': 0.076509, 'volaYearly': 0.13205
# with allocation
#avg longs per day: 6.343 , avg shorts per day: 6.746
d = {} ##Name of future : Close of all 88 futures
names = [] ##names of all 88 future
for i in range(0, nMarkets - 1):
n = markets[i + 1]
names.append(n)
d[n] = (CLOSE[i])
d_corr = settings['historic_corr']
## key = tuple of name of 2 futures, value = position to take for ((future1,future2),difference)
d_position = {}
for i in list(d_corr.keys()):
f = i[0]
s = i[1]
tup = d_corr[i]
l1 = d[f][-49:-1] ##take last 50 close
l2 = d[s][-49:-1] ##take last 50 close
corr, _ = pearsonr(l1, l2)
change_f = d[f][-2] - d[f][-49]
change_s = d[s][-2] - d[s][-49]
diff = tup - corr
if diff > 0.3:
if change_f > change_s:
d_position[i] = (
(-1, 1), diff
) ##assuming -1 means short while 1 means long
else:
d_position[i] = ((1, -1), diff)
for i in range(
len(names)): ##find position based on greatest variation
diff = 0
pair = tuple()
name = names[i]
counter = 0
for k in list(d_position.keys()):
if name in k:
counter += 1
pair = k
if counter == 1:
if name == k[0]:
if d_position[k][0][0] > 0:
pos[i + 1] = lweights[name]
else:
pos[i + 1] = sweights[name]
else:
if d_position[k][0][1] > 0:
pos[i + 1] = lweights[name]
else:
pos[i + 1] = sweights[name]
elif settings['model'] == 'FASTDTW':
#'sharpe': 4.8632971, 'sortino': 17.09129, 'returnYearly': 1.216714, 'volaYearly': 0.25018
# no allocation
# avg longs per day: 0.328 , avg shorts per day: 0.281
d = {} ##Name of future : Close of all 88 futures
names = [] ##names of all 88 future
for i in range(0, nMarkets - 1):
n = markets[i + 1]
names.append(n)
d[n] = (CLOSE[i])
d_dist = settings[
'historic_dist'] ## key = tuple of name of 2 futures, value = average distance
d_position = {}
for i in list(d_dist.keys()):
f = i[0]
s = i[1]
tup = d_dist[i]
l1 = d[f][-49:-1] ##take last 50 close
l2 = d[s][-49:-1] ##take last 50 close
distance, _ = fastdtw(l1, l2)
distance = distance / 50
change_f = d[f][-2] - d[f][-49]
change_s = d[s][-2] - d[s][-49]
diff = distance - tup
threshold = 16 * tup
if distance > threshold:
if change_f > change_s:
d_position[i] = (
(-1, 1), diff
) ##assuming -1 means short while 1 means long
else:
d_position[i] = ((1, -1), diff)
for i in range(
len(names)): ##find position based on greatest variation
diff = 0
name = names[i]
for k in list(d_position.keys()):
if name in k:
if d_position[k][1] > diff:
diff = d_position[k][1]
if name == k[0]:
if d_position[k][0][0] > 0:
pos[i + 1] = lweights[name]
else:
pos[i + 1] = sweights[name]
else:
if d_position[k][0][1] > 0:
pos[i + 1] = lweights[name]
else:
pos[i + 1] = sweights[name]
# check if latest economic data suggests downturn then activate short only strats
print("Positions:", pos)
if np.nansum(pos) > 0:
pos = pos / np.nansum(abs(pos))
settings['longs'] = settings['longs'] + sum(1 for x in pos if x > 0)
settings['shorts'] = settings['shorts'] + sum(1 for x in pos if x < 0)
settings['days'] = settings['days'] + 1
return pos, settings
