Smart Ribbon Model

Start 2020-01-01 00:00:00
End 2020-09-01 00:00:00
Duration 244 days 00:00:00
Exposure Time [%] 45.1195
Equity Final [$] 26592.8
Equity Peak [$] 27882
Return [%] 77.2854
Buy & Hold Return [%] 61.8654
Max. Drawdown [%] -14.9659
Avg. Drawdown [%] -2.54411
Max. Drawdown Duration 55 days 20:00:00
Avg. Drawdown Duration 6 days 07:00:00
# Trades 19
Win Rate [%] 31.5789
Best Trade [%] 40.001
Worst Trade [%] -7.93223
Avg. Trade [%] 4.53861
Max. Trade Duration 19 days 04:00:00
Avg. Trade Duration 5 days 16:00:00
Profit Factor 4.71101
Expectancy [%] 8.17065

This table presents a historical log of trading actions focused on Bitcoin, detailing key attributes involved in each trade. Each row represents a single trade and includes timestamps for the entry and exit points, the corresponding price at those moments, and the type of trade taken — either “Short” or “Long.” A **short** position indicates the trader expected the price to fall, while a **long** position means the trader was betting on a price increase. The table spans from July 2021 to December 2021 and captures both bullish and bearish market sentiments over time.

Notably, the table also includes a “return” or “profit/loss” column (last column), which reflects the percentage change achieved by each trade. For example, the trade entered on July 22, 2021, generated an impressive **59.94% return** from a long position, while other trades, such as the one on November 11, 2021, resulted in a loss of **-6.18%**. These fluctuations highlight the volatility and potential risk-reward dynamics of trading crypto assets using such strategies. The data shows a mix of small losses and large wins, which could suggest a strategy focused on minimizing risk while capturing outsized gains.

From an analysis perspective, this table is useful for evaluating the performance of a trading strategy over time. It allows for insights into trade timing, market direction accuracy, and profitability. The success of individual trades appears to vary depending on both entry points and trade direction, offering valuable lessons for optimizing model-driven or rule-based crypto trading systems.

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