How to Export BIT LangLang Trade History to Excel: A Review Method

First, Clarify: What Questions Should the Trade History Answer?

Many people's first reaction after exporting trade history is to calculate total P&L and win rate, then set it aside. But single-trade P&L is heavily influenced by market conditions—the same approach can yield completely different results in spring versus winter. What's truly worth finding in the records is whether the logic was clear when opening: what cycle were you in, why this direction, and where was the target? If these three things aren't added to the records, later statistics are meaningless.

So the first step isn't opening the spreadsheet—it's defining the review questions. For example: How many losing trades were counter-trend? How many were impulsive trades inside a range? How many were chasing highs with stops set too far? Once questions are set, the columns become meaningful. The BIT LangLang trading course on 2024-04-21 structured the trading system into three layers: cycle, entry point, and position sizing. When reviewing, you can also classify each trade by these three layers.

Export Raw Records from the Platform: Ensure Complete Fields

Major exchanges' futures accounts generally allow exporting trade records and order history, with fields including open/close time, contract, direction, average open price, average close price, quantity, fees, and realized P&L. Two things to note when exporting: First, the time range should cover a complete market cycle—don't just export the profitable days. Second, distinguish between opening and closing records; many platforms export individual fills, so you need to merge them into complete open-close cycles.

If you use this platform's strategy signals, you can compare the signal time with your own trades to see if execution deviated. This platform's strategies have generated 998 signals in total, of which 66 were actionable, and 27 actionable in the last 7 days. A mismatch between signal count and trade count often indicates missed orders, manual intervention, or duplicate positions—these deviations are themselves review material.

Add Three Columns in Excel: Cycle, Position, Logic

Raw fields only tell you what happened, not why. It's recommended to manually add three columns on the right side of the table. The first column is Cycle: what season was BTC in at the time—spring/summer with ample liquidity, or autumn/winter with lagging rallies and ebbing tides? The second column is Position: does this trade belong to initiation, small divergence, second wave after major divergence, top picking, or range bounce? The third column is Logic: write the reason for opening in one sentence; if you can't, mark it as 'no logic'.

Once these three columns are filled, many problems will surface on their own. For example, filter Position to 'range bounce' and look at P&L—you'll often find that such trades aren't profitable overall and drain energy. The BIT LangLang trading course on 2024-05-04 discussed waiting for only two types of opportunities inside a range and doing less otherwise. Use this to compare with your records during review—it's much more intuitive than looking at total P&L.

Statistics Should Be Categorized, Not Just Totals

Total P&L and overall win rate are the two most easily misread numbers. One big winning trade in a market cycle can cover the losses of ten small mistakes; the overall win rate might be below 50%, but the result is positive. So statistics should be broken down by category: trend-following vs. counter-trend, initiation vs. second wave, light position for testing vs. heavy position. After categorizing, look at P&L and drawdown for each category to know which practices are worth keeping.

BIT LangLang's historical trade history from 2022-05-24 to 2024-06-20 includes 4,807 trades across 129 coins, with average leverage 13.4x, win rate 40.8%, profit factor 2.28, and cumulative realized P&L 5.52M USDT. These numbers together reflect long-term accumulation under position sizing and low leverage, not the luck of a single trade. When organizing our own records, we should categorize using the same criteria, rather than using single-trade profits to prove the method is correct.

Turn Review Conclusions into the Next Trading Plan

After organizing the spreadsheet, the final step is output. Not a chart, but several actionable constraints: which types of entries to focus on next, maximum position size per trade, and how many consecutive losses before stopping. The BIT LangLang trading course on 2024-03-30 categorized losing trades into logic errors, position errors, and sizing errors, then tightened entry conditions accordingly. This approach can be directly applied: tag last period's losing trades into these three categories, and next period only take the category with the fewest tags.

Additionally, it's best to keep a column for 'emotional state at the time'—e.g., whether you reversed after consecutive losses, chased highs, or randomly reduced positions from staring at the screen too long. There are no gods in trading; emotional disturbance is normal. The key is to identify and adjust quickly. Write emotions into the records; next time you feel the same way, reviewing old sheets is far more reliable than making decisions on the spot.

After exporting trade history, which is more worth looking at: win rate or risk-reward ratio?

Both, but separately. A low win rate doesn't mean the method is bad—as long as the risk-reward ratio is high enough and losses are controlled, long-term results can be positive. What's more worth looking at is win rate and risk-reward ratio after categorization: win rate for trend-following, win rate for counter-trend, risk-reward ratio for different entry points. Mixing them together makes it easy to be misled by a few big trades.

There are many small impulsive trades in the records. Should I delete them before analyzing?

Not recommended. Impulsive trades are exactly what the review should catch—they drain emotions and energy and set the stage for later tilt. Keep them and tag them separately to see how much P&L they contributed and how much energy they consumed; this will remind you to do fewer trades without logic.

If I didn't record the opening logic, can I still review using only trade records?

You can review part of it, such as position and sizing, but the logic layer will be missing. The remedy is to place each trade back into the chart at the time, see whether it was at initiation, divergence, or inside a range, and then add your judgment. If you can't reconstruct it, mark it as unclear logic—such trades themselves are the ones to reduce.

How long does it take to organize trade history once, and how often should I do it?

The first time building the table is slower because you need to add columns and match charts—it might take an hour or two. After that, you only need to append new trades, which takes about ten minutes to update. In terms of frequency, it's suitable to do it after a market cycle ends or after consecutive losses; daily review is unnecessary and can easily trap you in details.