Strategy: BBMod1_2
Downloaded: 20220203
Stoploss: -0.99

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Average Overall
Trades/DayRejected Signals
Ninja Score: 61
The BBMod1 strategy is a trading strategy that uses various indicators and conditions to determine buying opportunities. Here is a summary of what the strategy does: Populate Indicators: This method calculates and adds additional indicators to the given dataframe, using both the current timeframe and a 1-hour informative timeframe. Populate Buy Trend: This method identifies buy signals based on several conditions.

Here are some of the important conditions used: is_dip: Checks for a potential dip in the market based on RMI (Relative Momentum Index), CCI (Commodity Channel Index), and SRSI (Slow Stochastic).

is_break: Looks for a breakout opportunity based on Bollinger Bands, delta, width, and price movement.

is_local_uptrend: Identifies a local uptrend by comparing exponential moving averages (EMA) and Bollinger Bands. is_local_uptrend2: Similar to is_local_uptrend but with different parameter values. is_local_dip: Searches for a local dip in the market using EMA, RSI (Relative Strength Index), CRSI (Commodity Relative Strength Index), and price movement. is_ewo: Examines EMA, RSI, and EWO (Elliott Wave Oscillator) for a potential buying opportunity. is_clucha: Considers various factors such as rate of change, Bollinger Bands, and candlestick patterns (HA: Heikin-Ashi) to find a favorable entry point. is_clucha2: Similar to is_clucha but with different parameter values. is_cofi: Uses EMA, fastk/fastd crossover, ADX (Average Directional Index), EWO, CTI (Candlestick Trend Index), and R_14 (Simple Moving Average) to identify a buying opportunity. is_nfi_32, is_nfi_33, is_nfi_38: These conditions represent different variations of NFI (Next Gen Financials Indicator) strategy, considering factors like RSI, EWO, CTI, R_14, and price levels. is_nfix_5, is_nfix_39, is_nfix_49: These conditions represent different variations of NFIX (Next Fix Indicator) strategy, incorporating EMA, BB (Bollinger Bands), delta, closedelta, tail, and EMA comparison. The strategy combines these conditions to determine when to generate buy signals. It assigns a "buy_tag" value to the dataframe to indicate potential buying opportunities.

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_49: 0.028%
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%
stoploss: -0.99
timeframe: 5m
hash(sha256): 12d88629c07cea8498f5a0acfd64a0b1cd4ad8cf13ae51ca33879565ccd93f30
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 ema_49 sma_21 momdiv_buy
bb_middleband2 closedelta sma_15 ha_open volume_mean_4
tcp_percent_4 avg_close_20 ema_24 cmf rmi_length_val
hma_50 vwap kc_lowerband_20_2 low

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last change: 2024-04-01 19:55:58