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The NostalgiaForInfinityV6 strategy is a trading strategy that involves several conditions and protections for buying assets. Here is a summary of its important components:
populate_indicators: This function populates the indicators used in the strategy, including informative 1-hour indicators and normal timeframe indicators. It merges the informative 1-hour data with the current timeframe data.
populate_buy_trend: This function defines the conditions and protections for buying assets.
It sets up multiple sets of protections and logic for each buy condition.
Buy Condition 1: It checks various protections such as exponential moving averages (EMA) of different lengths, close prices above EMAs, rising SMAs, safe dips, and safe pumps. It includes logic based on the close price's percentage change, RSI (relative strength index), MFI (money flow index), and trading volume. Buy Condition 2: It includes similar protections as in Buy Condition 1, but with different parameters. It adds logic based on the difference between RSI values of different timeframes, MFI, close price compared to the lower Bollinger Band, and trading volume. Buy Condition 3: It includes protections similar to the previous conditions, along with an additional condition based on the relative EMA value. It utilizes logic involving previous candlestick patterns, Bollinger Band delta, closed delta, tail size, and trading volume. Buy Condition 4: (The code was cut off before completion) Each buy condition has an enable flag, and if enabled, the corresponding conditions are added to the conditions list. The strategy combines the conditions using logical AND operations (&), reducing the conditions to a single boolean value. The resulting trigger values are stored in the dataframe for further processing. Overall, the NostalgiaForInfinityV6 strategy implements a complex set of conditions and protections to determine buying opportunities in trading.
populate_buy_trend: This function defines the conditions and protections for buying assets.
It sets up multiple sets of protections and logic for each buy condition.
Buy Condition 1: It checks various protections such as exponential moving averages (EMA) of different lengths, close prices above EMAs, rising SMAs, safe dips, and safe pumps. It includes logic based on the close price's percentage change, RSI (relative strength index), MFI (money flow index), and trading volume. Buy Condition 2: It includes similar protections as in Buy Condition 1, but with different parameters. It adds logic based on the difference between RSI values of different timeframes, MFI, close price compared to the lower Bollinger Band, and trading volume. Buy Condition 3: It includes protections similar to the previous conditions, along with an additional condition based on the relative EMA value. It utilizes logic involving previous candlestick patterns, Bollinger Band delta, closed delta, tail size, and trading volume. Buy Condition 4: (The code was cut off before completion) Each buy condition has an enable flag, and if enabled, the corresponding conditions are added to the conditions list. The strategy combines the conditions using logical AND operations (&), reducing the conditions to a single boolean value. The resulting trigger values are stored in the dataframe for further processing. Overall, the NostalgiaForInfinityV6 strategy implements a complex set of conditions and protections to determine buying opportunities in trading.
stoploss: -0.99 timeframe: 5m hash(sha256): db6c36906bd65bdfcb190e062d526b5488cb3f4c33bfa8eaeda167bba86a63fb indicators: sell_pump_36_2_1h sell_pump_36_3_1h sma_200_1h upper safe_pump_36_normal_1h ema_35_1h cmf_1h length ema_200 ema_50 sell_pump_36_1 sell_pump_24_1 close ema_15 sell_pump_36_2 tail safe_pump_36_strict sell_pump_24_1_1h bb_lowerband safe_pump_24_strict sma_5 safe_pump_48_strict mfi ema_200_1h bbdelta ewo safe_pump_48_normal volume sell_pump_36_3 ema_100_1h closedelta sell_pump_48_1 sell_pump_36_1_1h ema_20 open safe_pump_36_loose safe_dips_loose safe_dips_normal safe_dips_strict sell_pump_48_1_1h vo
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last change: 2023-04-26 08:06:12