Strategy: BBMod1
Downloaded: 20220124
Stoploss: -0.99
Downloaded: 20220124
Stoploss: -0.99
Showing 1 to 12 of 117 entries
| Buys | Avg Prof% | Cum Prof% | Tot Profit% | Avg Duration | Win | Draw | Loss | Win% | DD% | Stake | Exchange | Sharpe | Month |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 80 | 0.01 | 1.02 | 0.24 | 1:44:00 | 55 | 0 | 25 | 68.8 | 5.72 | USDT | kucoin | 0.2 | 202406 |
| 99 | 0.32 | 31.53 | 7.28 | 1:53:00 | 66 | 0 | 33 | 66.7 | 5.66 | USDT | kucoin | 7.7 | 202405 |
| 91 | -0.22 | -20.24 | -4.68 | 1:56:00 | 60 | 0 | 31 | 65.9 | 19.48 | USDT | kucoin | -3.47 | 202404 |
| 263 | 0.52 | 136.79 | 31.6 | 1:25:00 | 188 | 0 | 75 | 71.5 | 4.39 | USDT | kucoin | 32.48 | 202403 |
| 91 | 0.27 | 24.69 | 5.7 | 1:21:00 | 67 | 0 | 24 | 73.6 | 8.46 | USDT | kucoin | 5.14 | 202402 |
| 139 | 0.11 | 15.84 | 3.66 | 1:14:00 | 103 | 0 | 36 | 74.1 | 6.79 | USDT | kucoin | 4.08 | 202401 |
| 245 | 0.55 | 133.98 | 30.95 | 1:01:00 | 189 | 0 | 56 | 77.1 | 2.36 | USDT | kucoin | 45.78 | 202312 |
| 254 | 0.61 | 154.88 | 35.78 | 1:02:00 | 201 | 0 | 53 | 79.1 | 3.47 | USDT | kucoin | 54.54 | 202311 |
| 71 | 0.19 | 13.43 | 3.1 | 1:25:00 | 52 | 0 | 19 | 73.2 | 3.65 | USDT | kucoin | 3.35 | 202310 |
| 66 | 0.28 | 18.25 | 4.22 | 1:12:00 | 48 | 0 | 18 | 72.7 | 4.13 | USDT | kucoin | 3.91 | 202309 |
| 61 | -0.17 | -10.45 | -2.41 | 1:25:00 | 42 | 0 | 19 | 68.9 | 7.95 | USDT | kucoin | -2.44 | 202308 |
| 55 | 0.49 | 27.14 | 6.27 | 1:25:00 | 42 | 0 | 13 | 76.4 | 2.52 | USDT | kucoin | 10.95 | 202307 |
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The BBMod1 strategy is a trading strategy that involves backtesting and making buy decisions based on various technical indicators. Here is a summary of the strategy's important components:
populate_indicators: This function calculates and adds additional informative indicators to the input dataframe, such as indicators based on 1-hour data. These indicators are used for further analysis in the strategy.
populate_buy_trend: This function determines the conditions for initiating a buy trade.
It sets the buy_tag column of the dataframe based on different sets of conditions.
The following conditions are checked: is_dip: Checks if specific indicators (RMI, CCI, SRSI) are below certain threshold values. is_break: Checks if indicators related to Bollinger Bands (BB) and price movement meet certain criteria. is_local_uptrend: Checks if indicators related to local uptrends (EMA crossovers, BB, and price movement) meet certain criteria. is_local_uptrend2: Similar to is_local_uptrend, but with different parameter values. is_local_dip: Checks if indicators related to local dips (EMA crossovers, RSI, CRSI, and price movement) meet certain criteria. is_ewo: Checks if indicators related to EWO (Elliott Wave Oscillator), RSI, and EMA meet certain criteria. is_r_deadfish: Checks if indicators related to reverse deadfish pattern (EMA crossovers, BB, volume, CTI, and RSI) meet certain criteria. is_clucha: Checks if indicators related to Clucha pattern (ROCR, BB, and price movement) meet certain criteria. is_clucha2: Similar to is_clucha, but with different parameter values. is_cofi: Checks if indicators related to COFI pattern (EMA, FastK/FastD crossover, ADX, EWO, CTI, and RSI) meet certain criteria. is_nfi_32: Checks if indicators related to NFI-32 pattern (RSI, SMA, CTI, and price movement) meet certain criteria. is_nfi_33: Checks if indicators related to NFI-33 pattern (EMA, EWO, CTI, RSI, volume, and price movement) meet certain criteria. is_nfi_38: Checks if indicators related to NFI-38 pattern (PM, SMA, EWO, CTI, RSI, CRSI, and price movement) meet certain criteria. is_nfix_5: Checks if indicators related to NFIX-5 pattern (EMA, SMA, EWO, CTI, RSI, and price movement) meet certain criteria. is_nfix_39: (The description of this condition is incomplete in the provided code.) The strategy evaluates these conditions and assigns a specific buy_tag value to the dataframe based on which conditions are satisfied. The buy_tag will be used to determine when to execute a buy trade in the backtesting process.
populate_buy_trend: This function determines the conditions for initiating a buy trade.
It sets the buy_tag column of the dataframe based on different sets of conditions.
The following conditions are checked: is_dip: Checks if specific indicators (RMI, CCI, SRSI) are below certain threshold values. is_break: Checks if indicators related to Bollinger Bands (BB) and price movement meet certain criteria. is_local_uptrend: Checks if indicators related to local uptrends (EMA crossovers, BB, and price movement) meet certain criteria. is_local_uptrend2: Similar to is_local_uptrend, but with different parameter values. is_local_dip: Checks if indicators related to local dips (EMA crossovers, RSI, CRSI, and price movement) meet certain criteria. is_ewo: Checks if indicators related to EWO (Elliott Wave Oscillator), RSI, and EMA meet certain criteria. is_r_deadfish: Checks if indicators related to reverse deadfish pattern (EMA crossovers, BB, volume, CTI, and RSI) meet certain criteria. is_clucha: Checks if indicators related to Clucha pattern (ROCR, BB, and price movement) meet certain criteria. is_clucha2: Similar to is_clucha, but with different parameter values. is_cofi: Checks if indicators related to COFI pattern (EMA, FastK/FastD crossover, ADX, EWO, CTI, and RSI) meet certain criteria. is_nfi_32: Checks if indicators related to NFI-32 pattern (RSI, SMA, CTI, and price movement) meet certain criteria. is_nfi_33: Checks if indicators related to NFI-33 pattern (EMA, EWO, CTI, RSI, volume, and price movement) meet certain criteria. is_nfi_38: Checks if indicators related to NFI-38 pattern (PM, SMA, EWO, CTI, RSI, CRSI, and price movement) meet certain criteria. is_nfix_5: Checks if indicators related to NFIX-5 pattern (EMA, SMA, EWO, CTI, RSI, and price movement) meet certain criteria. is_nfix_39: (The description of this condition is incomplete in the provided code.) The strategy evaluates these conditions and assigns a specific buy_tag value to the dataframe based on which conditions are satisfied. The buy_tag will be used to determine when to execute a buy trade in the backtesting process.
startup_candle_count : 120 ema_50_1h: -0.009% ema_100_1h: 0.234% rsi_1h: 0.005% rmi_length_17: 0.002% srsi_fk: 0.409% srsi_fd: 0.410% hma_50: 0.020% ema_26: 0.001% ema_50: 0.030% ema_100: 0.094% rsi: -0.053% rsi_slow: -0.716% rsi_84: -13.138% ema_slow: 0.031% adx: 0.873% fisher: 0.929%
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 5m hash(sha256): 0eda269f4af00c9289e9a4ea03c5afe995ddeda9802ec9361ee0421bcbc1b18f indicators: upper close high_offset_2 volume_mean_12 ema_200_1h safe_dump_50 avg_val_20 rsi_fast volume xe1 ema_20 source ha_closedelta momdiv_coh trange bb_upperband2 volume_mean_24 high ema_8 r_480 cci bb_lowerband3 xe6 final_ub low_5 momdiv_col ha_high pm adx ema_12 sma_28 T3Average ema_slow ema_26 bb_lowerband2_40 bb_upperband2_40 basic_ub sma_21 ma_sell_val momdiv_buy bb_middleband2 closedelta sma_15 ha_open volume_mean_4 tcp_percent_4 avg_close_20 cmf rmi_length_val hma_50 vwap kc_lowerband_20_2 lower
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last change: 2025-01-13 23:46:28
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