Which time frame performing the best result after some back testing?

fastEMA:30 slowEMA:50 SOLUSDT SL=2.5, TP=1.8, return=13.29% in 1h.
fastEMA:30 slowEMA:50 SOLUSDT SL=2.5, TP=1.3, return=4.96% in 30m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.2, TP=1.1, return=-1.01% in 15m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.5, TP=2.5, return=-0.42% in 5m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.1, TP=1.4, return=0.66% in 4h.
fastEMA:20 slowEMA:40 SOLUSDT SL=2.4, TP=2.2, return=6.70% in 1h.
fastEMA:50 slowEMA:100 SOLUSDT SL=2.0, TP=2.5, return=8.82% in 1h.
fastEMA:30 slowEMA:50 ETHUSDT SL=2.1, TP=2.4, return=10.72% in 1h.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.8, TP=1.3, return=1.06% in 30m.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.1, TP=1.7, return=0.10% in 15m.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.3, TP=2.5, return=0.69% in 5m.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.0, TP=1.3, return=3.97% in 4h.
fastEMA:20 slowEMA:40 ETHUSDT SL=2.2, TP=2.4, return=9.95% in 1h.
fastEMA:50 slowEMA:100 ETHUSDT SL=1.3, TP=2.5, return=-2.24% in 1h.
def count_opened_trades():
api_instance = c.API(access_token)
config = api_instance.config
api_key = config.get(“api_key_bybit”)
api_secret = config.get(“api_secret_bybit”)
session = HTTP(testnet=False, api_key=api_key, api_secret=api_secret)
try:
data = session.get_positions(category=”linear”, symbol=Symbol)
size = data[‘result’][‘list’][0][‘size’]
return float(size)
except Exception as e:
print(f”DGM Failed to get {Symbol} position: {e}”)
return

def ema_signal(df, current_candle, backcandles):
df_slice = df.reset_index().copy()
start = max(0, current_candle – backcandles)
end = current_candle + 1
relevant_rows = df_slice.iloc[start:end]

# Check if all EMA_fast values are below EMA_slow values (buy signal)
if (relevant_rows[‘EMA_fast’] < relevant_rows[‘EMA_slow’]).all():
return 1
# Check if all EMA_fast values are above EMA_slow values (sell signal)
elif (relevant_rows[‘EMA_fast’] > relevant_rows[‘EMA_slow’]).all():
return -1
else:
return 0

def total_signal(df, current_candle, backcandles):
if isinstance(current_candle, pd.Timestamp):
current_candle = df.index.get_loc(current_candle)

ema_signal_result = ema_signal(df, current_candle, backcandles)

candle_open_price = df[‘Open’].iloc[current_candle]
bbl = df[‘BBL_15_1.5’].iloc[current_candle]
bbu = df[‘BBU_15_1.5’].iloc[current_candle]

if ema_signal_result == 1 and candle_open_price <= bbl:
return 1
if ema_signal_result == -1 and candle_open_price >= bbu:
return -1
return 0

def get_candles(symbol, interval, lookback):
url = f”{API_URLv3}{library}?symbol={symbol}&interval={interval}&limit={lookback}”
try:
response = requests.get(url)
response.raise_for_status()
data = response.json()

if not data:
print(f”No data received from {API_URLv3}{library} API.”)
return None

df = pd.DataFrame(data, columns=[
‘open_time’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’,
‘close_time’, ‘quote_asset_volume’, ‘number_of_trades’,
‘taker_buy_base_asset_volume’, ‘taker_buy_quote_asset_volume’, ‘ignore’
])
df[‘open_time’] = pd.to_datetime(df[‘open_time’], unit=’ms’)
df.set_index(‘open_time’, inplace=True)
df.rename(columns={‘open’: ‘Open’, ‘high’: ‘High’, ‘low’: ‘Low’, ‘close’: ‘Close’}, inplace=True)

df[[‘Open’, ‘High’, ‘Low’, ‘Close’]] = df[[‘Open’, ‘High’, ‘Low’, ‘Close’]].astype(float)
return df[[‘Open’, ‘High’, ‘Low’, ‘Close’]]
except requests.RequestException as e:
print(f”{symbol} Request failed: {e}”)
except Exception as e:
print(f”Failed to process {symbol} data: {e}”)
return None

def get_candles_frame(lookback):
candles = get_candles(Symbol, Interval, lookback)

if candles is None:
print(f”Failed to retrieve {Interval} {Symbol} candle data.”)
return None

dfstream = candles.copy()

dfstream[‘ATR’] = ta.atr(dfstream[‘High’], dfstream[‘Low’], dfstream[‘Close’], length=7)
dfstream[‘EMA_fast’] = ta.ema(dfstream[‘Close’], length=30)
dfstream[‘EMA_slow’] = ta.ema(dfstream[‘Close’], length=50)
dfstream[‘RSI’] = ta.rsi(dfstream[‘Close’], length=10)
my_bbands = ta.bbands(dfstream[‘Close’], length=15, std=1.5)
dfstream = dfstream.join(my_bbands)
if not isinstance(dfstream.index, pd.DatetimeIndex):
dfstream.index = pd.to_datetime(dfstream.index)

dfstream[‘TotalSignal’] = dfstream.apply(lambda row: total_signal(dfstream, row.name, 7), axis=1)
dfstream.to_csv(‘dfstream.csv’)
return dfstream

def optimization():
slatrcoef = 0
TPSLRatio_coef = 0

dfstream = get_candles_frame(lookback)
if dfstream is None:
print(f”No candle data for {Symbol} fitting optimization job.”)
return

def SIGNAL():
return dfstream[‘TotalSignal’]

class MyStrat(Strategy):
mysize = 3000
slcoef = 1.3
TPSLRatio = 2.5

def init(self):
self.signal1 = self.I(SIGNAL)

def next(self):
slatr = self.slcoef * self.data.ATR[-1]
TPSLRatio = self.TPSLRatio

if self.signal1[-1] == 2 and len(self.trades) == 0:
sl1 = self.data.Close[-1] – slatr
tp1 = self.data.Close[-1] + slatr * TPSLRatio
self.buy(sl=sl1, tp=tp1, size=self.mysize)

elif self.signal1[-1] == 1 and len(self.trades) == 0:
sl1 = self.data.Close[-1] + slatr
tp1 = self.data.Close[-1] – slatr * TPSLRatio
self.sell(sl=sl1, tp=tp1, size=self.mysize)

bt = Backtest(dfstream, MyStrat, cash=100000, margin=0.01, commission=0.00055)
stats, heatmap = bt.optimize(slcoef=[i/10 for i in range(10, 26)],
TPSLRatio=[i/10 for i in range(10, 26)],
maximize=’Return [%]’, max_tries=300,
random_state=0,
return_heatmap=True)
print(stats)

slatrcoef = stats[“_strategy”].slcoef
TPSLRatio_coef = stats[“_strategy”].TPSLRatio
print(f”{Symbol} SL = {slatrcoef}, TP = {TPSLRatio_coef}, expected return, {stats[‘Return [%]’]:.2f}% in {Interval} interval.\n”)

with open(“fitting_data_file.txt”, “a”) as file:
file.write(f”{Symbol} SL = {slatrcoef}, TP = {TPSLRatio_coef}, expected return, {stats[‘Return [%]’]:.2f}% in {Interval} interval.\n”)
return slatrcoef, TPSLRatio_coef

def trading_job():
dfstream = get_candles_frame(lookback)
if dfstream is None:
print(f”No {Symbol} candle data for trading job.”)
return

signal = total_signal(dfstream, len(dfstream) – 1, 7)

# now = datetime.now()
# if now.weekday() == 0 and now.hour < 7 and now.minute < 5: # Monday before 07:05
slatrcoef, TPSLRatio_coef = optimization()
print(f”Optimize SL = {slatrcoef}, and TP = {TPSLRatio_coef}.”)

slatr = slatrcoef * dfstream[‘ATR’].iloc[-1]
TPSLRatio = TPSLRatio_coef
max_spread = 16e-5

last_candle = get_candles(Symbol, Interval, 1).iloc[-1]
candle_open_bid = float(last_candle[‘Open’])
candle_open_ask = candle_open_bid
spread = candle_open_ask – candle_open_bid

SLBuy = candle_open_bid – slatr – spread
SLSell = candle_open_ask + slatr + spread

TPBuy = candle_open_ask + slatr * TPSLRatio + spread
TPSell = candle_open_bid – slatr * TPSLRatio – spread

print(“SLBuy = “, SLBuy)
print(“SLSell = “, SLSell)
print(“TPBuy = “, TPBuy )
print(“TPSell = “, TPSell)

# # Sell
# if signal == -1 and count_opened_trades() == 0.0 and spread < max_spread:
# print(“Sell Signal Found…”)
# trade_crypto = c.TradeCrypto(EXCHANGE, Symbol, ‘sell’)
# message, MyTradePrice = trade_crypto.TradeQty(quantity)
# print(message)
# with open(“trading_data_file.txt”, “a”) as file:
# file.write(f”SL = {SLSell}, TP = {TPSell}, Trade Price = {MyTradePrice}\n”)

# # Buy
# elif signal == 1 and count_opened_trades() == 0.0 and spread < max_spread:
# print(“Buy Signal Found…”)
# trade_crypto = c.TradeCrypto(EXCHANGE, Symbol, ‘buy’)
# message, MyTradePrice = trade_crypto.TradeQty(quantity)
# print(message)
# with open(“trading_data_file.txt”, “a”) as file:
# file.write(f”SL = {SLBuy}, TP = {TPBuy}, Trade Price = {MyTradePrice}\n”)

if __name__ == “__main__”:
optimization()

# scheduler = BlockingScheduler()
# scheduler.add_job(trading_job, ‘cron’, day_of_week=’mon-fri’, hour=’07-18′, minute=’1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56′, timezone=’Asia/Beirut’, misfire_grace_time=15)
# scheduler.start()

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