BTC bot

This trading strategy is a long/short bot for BTC that takes positions based on a combination of pivot points calculated from a higher time-frame, and a custom RSI-based indicator calculated on the lower time-frame. Buy signals are generated on a crossover of the custom indicator above zero, and sell signals are generated when the price crosses over the R3 pivot point level.

To trade this strategy manually using TradingView:

• Set the time frame to weekly (W) to calculate the pivot points.
• Determine the Pivot Point (PP), Resistance (R1, R2, R3), and Support (S1, S2, S3) levels using the previous week's high, low, and close prices.
• Calculate a 21-period RSI of the close price on your desired trading time-frame.
• Then calculate an EMA of the same length on the RSI value to find the RSI trend.
• Create a composite indicator by multiplying the difference between the RSI and its EMA by 5, adding this to the RSI, and averaging the result with the previous step's calculated EMA of the RSI.
• Buy (go long) when the composite indicator crosses above zero.
• Sell (go short) when the current price crosses over the R3 pivot level.
• Implement a stop loss of 15% from the entry price.
• Use three take profit levels at 3%, 5%, and 7% above the entry price, and adjust the position size according to the defined quantities (25% for each take profit level).
• Maintain the rest of the position, or close it, at a 10% profit level or a stop loss.

Improving a trading strategy often involves refining entry and exit points, enhancing risk management, and adapting to various market conditions. The BTC bot strategy can be manually adjusted to increase its efficacy. Here’s a concise plan on how to enhance this strategy manually:

• Refine Entry Points: Integrate market structure analysis to confirm signals generated by the Low finder script. Look for confluences with significant support levels, trend lines, or Fibonacci retracement levels to strengthen the entry signal. Wait for additional confirmation from price action, such as bullish candlestick patterns for long entries.
• Optimize Exit Points: Instead of fixed take profit levels, consider dynamic exits based on changing market conditions. Utilize trailing stops to lock in profits while allowing winning trades to run. Analyze the price action near pivot points and adjust take profit levels to just below significant resistance levels to preempt possible reversals.
• Adapt to Market Trends: Gauge the overall market trend using higher time frame analysis to determine whether the market is in a bullish or bearish phase. Adjust the strategy's bias towards long or short to align with the prevailing trend. In a bear market, decrease position size or increase the standards for trade entries to avoid false signals.
• Vary Position Sizing: Employ a tiered position sizing method where the size of the trade correlates with the strength of the signal. If multiple confirmation factors are present, increase the position size within risk management limits. Conversely, reduce the size when fewer confirmatory signals are evident.
• Tighten Risk Management: Reassess the stop-loss strategy by incorporating Average True Range (ATR) to set stop levels that reflect current market volatility. Use a risk-reward ratio at a minimum of 1:2 to ensure that potential gains justify the risk taken on each trade.
• Enhance Signal Processing: Until the bot's ability to utilize signals from alerts is fully functional, manually monitor price alerts set at critical levels. Use multiple time-frame analysis and oscillators like MACD or Stochastic to add depth to the signal detection process.
• Periodic Backtesting and Optimization: Regularly backtest the refined strategy against historical data to ensure its validity and make adjustments as necessary. Journal trades to identify patterns in successful and unsuccessful trades for continuous improvement.

This trading strategy is ideal for day traders and swing traders who specialize in cryptocurrency markets, particularly Bitcoin. It's designed for traders who prefer technical analysis, utilizing indicators and price action to identify entry and exit points. The approach suits those comfortable with:

• Active management — manually adjusting trade parameters as market conditions change.
• Computational tools — using scripts to inform decision-making, although full automation is not yet in place.
• Pivot-based strategies — capitalizing on significant price levels to determine trade exits.

The style also caters to traders who can cope with bullish market biases but wish to develop their skills further to adapt to bearish trends. Moreover, it demands a willingness to continuously monitor and manually tweak trades, given the bot's current limitation in responding to real-time alerts.

• Strategy Essence: A BTC-focused technical strategy harnessing pivot points for exits and a Low finder script for entries, with an inclination towards long positions.
• Operational Mode: Utilizes computational scripts suitable for day and swing traders, though full automation is not available due to current alert signal limitations.
• Custom Indicators: Employs a composite index based on RSI and EMA for trade signals and relies on predefined pivot levels for exits.
• Manual Intervention: Requires manual trade monitoring and adjustment, especially in the absence of automated real-time alert processing.
• Improvement Strategies: Involve refining entry/exits with market structure analysis, using dynamic exits with trailing stops, and varying position sizes based on signal strength.
• Risk Adjustment: Better risk management through ATR-informed stop losses and maintaining a minimum 1:2 risk-reward ratio.
• Trend-Sensitivity: Adapts well to bullish scenarios but requires caution and adjustments when trading in bearish trends.
• Continuous Optimization: Encourages periodic strategy backtesting and the use of trading journals for ongoing enhancement.