Everything You Need to Know About Your Trading Data
Every trade your bot executes is permanently recorded in your BitcoinEra account. This data is more than a historical curiosity — it’s the raw material for understanding how your strategy is actually performing, identifying patterns that need attention, preparing accurate tax records, and making informed decisions about future configuration.
Most users glance at their trade history occasionally but never extract its full value. This guide changes that. We’ll cover every aspect of the trade history system — what data is recorded, how to read it correctly, how to filter and analyze it effectively, how to export it for external use, and how to use it for tax reporting.
What Data Is Recorded
For every trade your bot executes, BitcoinEra automatically records the following data points:
Trade Identification
Trade ID: A unique identifier assigned to every trade by BitcoinEra. Used for support queries — if you need to report an issue with a specific trade, reference its Trade ID.
Exchange Order ID: The order identifier assigned by your exchange when the trade was executed. This allows you to cross-reference BitcoinEra’s records with your exchange’s own trade history — important for reconciliation and tax purposes.
Bot Name: Which bot executed this trade. Critical when running multiple bots simultaneously.
Strategy Type: The strategy category of the bot that made this trade — Grid, DCA, Trend Following, etc.
Timing Data
Open Timestamp: The exact date and time the position was opened — when the entry order was filled. Recorded in UTC and displayed in your configured local timezone.
Close Timestamp: The exact date and time the position was closed — when the exit order was filled.
Trade Duration: The calculated time between open and close timestamps — displayed in hours and minutes for short trades, days and hours for longer ones.
Day of Week / Time of Day: While not always displayed directly, this data is present in the timestamps and becomes relevant for pattern analysis — discussed later in this guide.
Price Data
Entry Price: The exact price at which Bitcoin was purchased (long) or sold short at the time the position opened. For limit orders — the exact limit price. For market orders — the actual execution price including any slippage from the quoted price.
Exit Price: The exact price at which the position closed — at take profit, stop loss, trailing stop, or manual exit.
Highest Price During Trade: The highest Bitcoin price recorded while the trade was open — useful for analyzing trailing stop efficiency and missed opportunity analysis.
Lowest Price During Trade: The lowest Bitcoin price recorded while the trade was open — useful for analyzing stop loss placement and drawdown exposure per trade.
Size and Capital Data
Trade Size (USDT): The dollar value of capital deployed in this trade.
Bitcoin Amount: The quantity of Bitcoin bought or sold — calculated from trade size divided by entry price.
Allocated Capital at Trade Time: What your total allocation was when this trade executed — provides context for understanding trade size as a percentage of total allocation.
Financial Outcome Data
Gross Profit/Loss (USDT): The raw profit or loss from price movement — before fees.
Long trade: (Exit Price - Entry Price) × Bitcoin Amount
Short trade: (Entry Price - Exit Price) × Bitcoin AmountGross Profit/Loss (%): The percentage return on the capital deployed in this trade — before fees.
Gross P&L % = (Exit Price - Entry Price) / Entry Price × 100Entry Fee: The exchange fee charged when the position was opened.
Entry Fee = Trade Size × Exchange Fee RateExit Fee: The exchange fee charged when the position was closed.
Exit Fee = (Trade Size + Gross P&L) × Exchange Fee RateTotal Fees: Combined entry and exit fees for this trade.
Net Profit/Loss (USDT): The actual realized profit or loss after all fees.
Net P&L = Gross P&L - Total FeesNet Profit/Loss (%): The actual percentage return after fees.
Net P&L % = Net P&L / Trade Size × 100⚠️ Always use Net P&L — never Gross P&L — when evaluating actual performance. Fee impact is real and sometimes significant, especially for high-frequency strategies.
Exit Classification Data
Exit Reason: How the trade closed:
TAKE_PROFIT— closed at the configured profit targetSTOP_LOSS— closed at the configured stop loss levelTRAILING_STOP— closed when trailing stop was triggeredDRAWDOWN_LIMIT— closed because the bot’s drawdown limit was reachedDAILY_LOSS_LIMIT— closed because the daily loss limit was reachedMANUAL_CLOSE— manually closed by you from the dashboardBOT_STRATEGY— closed by the bot’s internal strategy logic (grid rebalancing, DCA cycle completion, etc.)API_ERROR— closed due to a technical error (investigate immediately if you see this)
Exit Classification: Whether the trade was a Winner (positive net P&L) or Loser (negative net P&L).
Navigating the Trade History Interface
Default View
By default, the trade history shows your most recent 50 trades across all bots — sorted by close timestamp with the most recent first.
The table columns visible by default:
- Close Timestamp
- Bot Name
- Direction
- Entry Price
- Exit Price
- Trade Size
- Net P&L (USDT)
- Net P&L (%)
- Exit Reason
Additional columns can be added by clicking the column selector — useful for deeper analysis.
Pagination and Load More
The trade history loads 50 records at a time. Use pagination controls to navigate through older records or click “Load All” to load your complete history — note that this may take a moment for accounts with extensive trade histories.
Filtering Controls
Date Range Filter
Options:
- Today
- Last 7 Days
- Last 30 Days
- Last 90 Days
- This Month
- Last Month
- Custom Range (specify exact dates)
Best use cases:
- Monthly tax reporting: use “Last Month” or custom range for the specific month
- Performance review: use “Last 30 Days” for monthly performance
- Specific event analysis: use custom range to isolate a particular market event period
Bot Filter
Select one or multiple bots to show only their trades. Essential when running multiple bots — without filtering, the combined history can make it difficult to evaluate any individual bot’s behavior.
Direction Filter
Filter by Long, Short, or both. Useful for strategies that trade both directions — allows separate analysis of long and short performance.
Exit Reason Filter
Filter to show only specific exit types:
- Show only Stop Loss exits: reveals the frequency and size of your losses
- Show only Take Profit exits: reveals your winning trade distribution
- Show only Trailing Stop exits: useful for optimizing trailing stop width
P&L Filter
Filter by outcome:
- Winners only
- Losers only
- Above/below specific P&L threshold
Useful for identifying outlier trades — both your best performers and worst losses.
Trade Size Filter
Filter by trade size range — useful for identifying whether position sizing has been consistent or variable.
Sorting Controls
Click any column header to sort by that field. Useful sorts:
Sort by Net P&L descending: Find your best performing trades. Sort by Net P&L ascending: Find your worst performing trades. Sort by Trade Duration descending: Find your longest-held positions. Sort by Trade Size descending: Find your largest positions. Sort by Entry Price: Group trades by price level — useful for identifying clustering around specific levels.
Performance Analysis Using Trade History
Beyond simply reviewing what trades happened — the trade history contains information that allows systematic performance analysis. Here’s how to extract maximum insight.
Win Rate Analysis
Basic calculation:
Win Rate = Count of Winner trades / Total Closed Trades × 100Time-period win rate comparison: Calculate win rate for different time periods and compare:
- Is win rate consistent month over month?
- Did win rate change significantly after a market condition shift?
- Is win rate consistent across different days of the week or times of day?
A declining win rate over recent weeks — compared to your historical average — is one of the first indicators that a strategy may be misaligning with current market conditions.
Average Win/Loss Analysis
Average win size:
Average Win = Sum of all positive Net P&L / Count of Winner tradesAverage loss size:
Average Loss = Sum of all negative Net P&L (as absolute value) / Count of Loser tradesWin/Loss ratio:
Win/Loss Ratio = Average Win / Average LossWhy this matters more than win rate alone: A win rate of 40% with a Win/Loss ratio of 3.0 means you win $3 for every $1 you lose — highly profitable despite losing more often than winning.
A win rate of 70% with a Win/Loss ratio of 0.4 means you lose $2.50 for every $1 you win — unprofitable despite winning frequently.
Always analyze win rate and Win/Loss ratio together.
Profit Factor Calculation
Formula:
Profit Factor = Sum of all positive Net P&L / Sum of all negative Net P&L (absolute values)Target values by strategy:
| Strategy | Healthy Profit Factor |
|---|---|
| Grid Trading | 1.8 – 3.0 |
| DCA | 2.0 – 4.0 |
| Trend Following | 1.5 – 2.5 |
| Breakout | 1.4 – 2.5 |
| Scalping | 1.3 – 2.0 |
| Mean Reversion/RSI | 1.5 – 2.5 |
Exit Reason Distribution Analysis
Count the frequency of each exit reason and calculate percentages:
Exit Reason Distribution:
Take Profit: 58% of trades
Stop Loss: 31% of trades
Trailing Stop: 8% of trades
Manual Close: 3% of tradesWhat each distribution tells you:
High Take Profit %: Strategy is finding and executing profitable setups — favorable conditions.
High Stop Loss %: Strategy is frequently stopped out — possible causes:
- Stop loss set too tight relative to volatility
- Strategy misaligned with current market conditions
- Entry timing needs improvement
High Trailing Stop %: Strategy is capturing extended moves and letting them run — positive indicator for trend following bots.
High Manual Close %: You’re frequently overriding the bot’s logic. Track whether manual closes outperform or underperform the automated exits — the data will tell you whether your manual interventions are adding value.
Any API_ERROR exits: Investigate immediately — these indicate technical issues that need resolution.
Trade Duration Analysis
Calculate average trade duration by exit type:
Average duration for Take Profit exits: typically longer (trade had time to reach target) Average duration for Stop Loss exits: may be shorter (stopped out quickly on adverse moves) Average duration for Trailing Stop exits: typically longest (trade ran with momentum)
Compare to strategy expectations:
If your trend following bot’s average trade duration is 2 hours — but the strategy description says it’s designed for daily timeframes — the bot may be using a shorter timeframe than expected or stop losses are triggering too early.
Time-Based Performance Patterns
Using the timestamp data, analyze whether performance varies by time period:
Day of week analysis: Calculate average Net P&L per trade by day of week. Do specific days consistently produce better or worse results? Crypto markets have documented patterns of lower liquidity and higher volatility on weekends — this may affect strategy performance.
Time of day analysis: Calculate average Net P&L by hour of day (UTC). Bitcoin trading volume is highest during European and US market hours. For strategies that depend on liquidity — analyzing time-of-day performance can reveal whether limiting active trading to high-volume hours improves results.
Monthly seasonality: Some Bitcoin trading patterns have seasonal characteristics. Comparing performance across months can identify whether the strategy has systematic weaknesses in specific calendar periods.
Consecutive Loss Streak Analysis
What it is: Identifying the maximum number of consecutive losing trades in your history.
How to find it: Sort trade history chronologically. Scan through looking for sequences of Loser trades without a Winner in between.
Why it matters: Your maximum consecutive loss streak tells you how many losing trades in a row you need to psychologically and financially tolerate before a winning trade recovers the losses.
If your bot’s history shows a maximum consecutive loss streak of 7 — and your current configuration can absorb 7 consecutive stop loss hits without triggering the drawdown limit — you’re within historical parameters. If 7 consecutive losses would exhaust your capital before the drawdown limit — your position sizing is too aggressive.
Exporting Trade History
BitcoinEra provides comprehensive export functionality for all trade history data.
Export Formats
CSV Export
Best for:
- Importing into Excel or Google Sheets for custom analysis
- Tax software that accepts CSV input
- Custom database or accounting systems
How to export: Trade History → Export → CSV → Select date range → Select bots (all or specific) → Download
CSV columns included: All data fields listed in the “What Data Is Recorded” section above — complete and unfiltered.
PDF Export
Best for:
- Human-readable performance reports
- Sharing with an accountant or financial advisor
- Personal record-keeping
What’s included: A formatted report containing:
- Summary statistics for the period
- Performance charts
- Trade log table
- Key metrics summary
How to export: Trade History → Export → PDF → Select date range → Select bots → Choose report format (Summary or Detailed) → Generate → Download
Excel Export
Best for:
- Pre-formatted spreadsheet with formulas already built in
- Users who prefer Excel over raw CSV
What’s included: Multiple worksheets:
- Summary Dashboard tab
- Raw Trade Data tab
- Performance Analysis tab (pre-built formulas)
- Fee Analysis tab
- Monthly Breakdown tab
How to export: Trade History → Export → Excel → Select date range → Download
Scheduled Exports
For users who want regular automated exports — BitcoinEra supports scheduled export delivery:
Options:
- Daily summary email with previous day’s trade data
- Weekly CSV attachment every Monday
- Monthly full export on the 1st of each month
How to set up: Account Settings → Reporting → Scheduled Exports → Configure frequency and format → Save
API Access for Trade Data
For technically advanced users — BitcoinEra provides a read-only API endpoint for accessing trade history programmatically.
Use cases:
- Custom analysis applications
- Integration with personal accounting software
- Automated reporting systems
- Building custom dashboards
Authentication: Requires a BitcoinEra Read API key — separate from your exchange API keys. Generate in Account Settings → Security → BitcoinEra API Keys.
Documentation: Full API documentation available at bitcoinera.biz/api/docs
Tax Reporting
Cryptocurrency trading generates taxable events in most jurisdictions. The specifics vary significantly by country — but in most cases each closed trade is a taxable event requiring accurate reporting of:
- Date of acquisition (entry date)
- Date of disposal (exit date)
- Proceeds (exit value)
- Cost basis (entry value + fees)
- Gain or loss (proceeds minus cost basis)
⚠️ BitcoinEra does not provide tax advice. The information below is general guidance only. Consult a qualified tax professional in your jurisdiction for advice specific to your situation.
Data You Need for Tax Reporting
For each closed trade:
- Open timestamp (date of acquisition)
- Close timestamp (date of disposal)
- Bitcoin amount (quantity)
- Entry price (cost per unit)
- Exit price (proceeds per unit)
- Entry fee (transaction cost at acquisition)
- Exit fee (transaction cost at disposal)
- Net P&L (gain or loss)
All of this data is available in your BitcoinEra trade history export.
How to Prepare a Tax Export
Step 1 — Determine your tax year: Different jurisdictions have different tax year boundaries — January–December, April–March, July–June. Export your trade history for your specific tax year.
Step 2 — Export complete history: Trade History → Export → CSV → Custom Range → Your tax year start to end date → All bots → Download
Step 3 — Verify completeness: Cross-reference the total trade count in your export against the trade count shown in your dashboard for the same period. They should match.
Step 4 — Cross-reference with exchange records: Compare a sample of trades in your BitcoinEra export against the corresponding records in your exchange’s own trade history. The Exchange Order ID in each trade record allows precise matching.
Step 5 — Import into tax software or provide to accountant: Most cryptocurrency tax software (Koinly, CoinTracker, TaxBit, etc.) accepts CSV imports. Provide the BitcoinEra CSV export alongside any other crypto activity records.
Tax Considerations for High-Frequency Bots
Grid trading and scalping bots can generate hundreds or thousands of taxable events per year — each one requiring individual reporting in most jurisdictions.
Important considerations:
Volume: A grid bot making 10 trades per day generates approximately 3,650 trades per year — all potentially taxable. Manual calculation is impractical. Use crypto tax software that handles high-volume imports.
Short-term vs long-term treatment: Most bot trades close within days or weeks — qualifying as short-term capital gains in most jurisdictions (typically taxed at higher rates than long-term gains). Consult your tax advisor about the implications.
FIFO vs LIFO vs Average Cost: Different jurisdictions require different cost basis calculation methods. Ensure your tax software is configured for your jurisdiction’s requirements.
Fee deductibility: Exchange fees are typically deductible as transaction costs — they reduce your taxable gain or increase your deductible loss. Verify this applies in your jurisdiction.
Reconciliation — Ensuring Your Records Are Accurate
Before submitting any tax filing — reconcile your BitcoinEra records with your exchange records.
Reconciliation process:
Step 1 — Export BitcoinEra trade history for the period
Step 2 — Export your exchange trade history for the same period All three supported exchanges provide trade history exports:
- Binance: Account → Order History → Export
- Bybit: Assets → Account History → Export
- OKX: Trade History → Export
Step 3 — Match records using Exchange Order ID Every trade in BitcoinEra has a corresponding Exchange Order ID. Match each BitcoinEra trade to the corresponding exchange record. They should have identical prices, sizes, and timestamps.
Step 4 — Investigate discrepancies If any trades appear in the exchange export but not in BitcoinEra — or vice versa — investigate the cause. Common reasons:
- Manual trades placed directly on the exchange (not through the bot) appear in exchange history but not BitcoinEra
- Bot trades during a brief connection interruption may appear differently in each system
Step 5 — Document reconciliation results Keep a record of your reconciliation process — both the output and any discrepancies found and resolved. This documentation is valuable if your tax filing is ever audited.
Performance Reporting — Creating Meaningful Reports
Beyond tax reporting — generating regular performance reports helps you track progress, make informed decisions, and maintain accountability to your own investment goals.
Monthly Performance Report
What to include:
Period Summary:
- Total trades executed
- Win rate for period
- Profit factor for period
- Net P&L for period
- Maximum drawdown during period
- Fees paid during period
Bot-by-Bot Breakdown:
- Individual performance metrics for each running bot
- Comparison to each bot’s historical averages
- Notable events (drawdown limit triggers, large wins, large losses)
Market Context:
- Brief note on Bitcoin price movement during the period
- Assessment of whether conditions were favorable or unfavorable for each strategy
- Any significant news events that affected performance
Forward Assessment:
- Are current market conditions still aligned with each running strategy?
- Any configuration adjustments planned based on performance data?
- Capital allocation changes being considered?
How to generate it: Use the BitcoinEra PDF export for the period as the base — then add your own context and assessment in a personal document or spreadsheet alongside it.
Annual Performance Review
Once per year — a comprehensive review of the full year’s data provides the clearest picture of your bot trading operation’s actual performance and informs strategy decisions for the year ahead.
Annual review components:
Full year P&L by month: A month-by-month breakdown revealing seasonal patterns, best and worst months, and overall trajectory.
Strategy performance comparison: If you ran multiple strategies — compare them on equal terms across the full year. Which performed best? Which struggled in which conditions?
Fee analysis: Total fees paid across the year. For high-frequency strategies — annual fees can be a significant portion of gross returns. Understanding the fee impact helps evaluate whether fee optimization (achieving higher volume tiers, using fee-reducing tokens) would be worthwhile.
Drawdown analysis: Maximum drawdown experienced, duration of drawdown periods, recovery time. Compare to the strategies’ historical parameters.
Capital efficiency: Return on total capital allocated across all bots. Compare to simple Bitcoin holding performance for the same period — this is your opportunity cost benchmark.
Lessons and adjustments: What did you learn from the year’s performance? What would you configure differently? What market conditions surprised you?
Summary
Here’s everything we covered in this guide:
- Complete documentation of every data point recorded for each trade
- How to navigate and filter the trade history interface effectively
- Performance analysis techniques — win rate, average win/loss, profit factor, exit distribution, time patterns
- How to identify consecutive loss streaks and verify your configuration can handle them
- Export formats — CSV, PDF, Excel — and when to use each
- Scheduled exports and API access for advanced users
- Tax reporting — the data you need, how to prepare it, and considerations for high-frequency bots
- Reconciliation process — ensuring BitcoinEra and exchange records match
- Monthly and annual performance report frameworks
⚠️ Tax Disclaimer: BitcoinEra does not provide tax, legal, or financial advice. Tax treatment of cryptocurrency trading varies significantly by jurisdiction and individual circumstances. Consult a qualified tax professional in your jurisdiction before preparing any tax filing related to your trading activity.
