Tradezella has emerged as one of the most powerful journaling and performance analytics platforms for traders. Beyond simple P/L tracking, it provides deep insights into win rate, risk–reward distribution, expectancy, and execution quality. Features like trade tagging, setup categorization, and psychological review allow traders to not only measure results but also diagnose behavior patterns and decision quality. Its dashboard gives a clear picture of consistency and equity curve health, making it especially useful for refining sizing, scaling strategies, and identifying which setups deserve more capital allocation. When used systematically, Tradezella acts as both a mirror and a compass—helping traders see their strengths with clarity while pointing toward areas for continuous growth.
We recommend serious traders to review and use this tool for their deep analysis and development.
Some of the metrics available in tradezella reports
* Total P&L: The total realized Profit and Loss (P/L) on all closed positions, for the date range selected.
* Average Daily Volume: The average volume, or quantity of contracts/shares traded on a daily basis.
* Average Winning Trade: The average profit on your winning trades. In other words, how much do you make on average, on your winning trades?
* Average Losing Trade: The average loss on your losing trades. In other words, how much do you lose on average, on your losing trades?
* Total Number of Trades: Total number of closed trades for the defined date range.
* Number of Winning Trades: Total number of winning trades for the defined date range. A winning trade is defined as a trade with a positive P&L.
* Number of Losing Trades: Total number of losing trades for the defined date range. A losing trade is defined as a trade with a negative P&L.
* Number of Break Even Trades: Total number of trades where you broke ''even'', where the P/L is equal to or approximately $0.
* Maximum Consecutive Wins: The number of consistent winning trades in a row.
* Maximum Consecutive Losses: The number of consistent losing trades in a row.
* Total Commissions: Total amount spent on commissions.
Note: This information is only available if your broker includes it in their CSV files.
* Total Fees: Total amount spent on fees.
* Total Swap: Total amount spent on Swaps.
* Largest Profit: Your largest profitable trade.
* Largest Loss: The largest amount lost on a trade.
* Average Hold Time (All Trades): Your average hold time, or how long you were in a trade, is based on ALL trades taken.
* Average Hold Time (Winning Trades): Your average hold time, or how long you were in a trade, is based on your winning trades. Example: On average, you hold your winning trades for longer than a day.
* Average Hold Time (Losing Trades): Your average hold time, or how long you were in a trade, is based on your losing trades. Example: On average, you hold you're losing trades for 3 hours.
* Average Hold Time (Scratch Trades): Your average hold time, or how long you were in a trade, based on trades where you broke even.
* Average Trade P&L: The average P/L based on all your trades. What is your usual trade outcome? How much do you make on average on each trade?
* Profit Factor: Total profits divided by total losses. This is a measure of how profitable your trading system is.
* Open Trades: Number of open trades, or trades that have not been fully closed yet.
* Total Trading Days: Total number of trading days. A trading day is defined as a day when a trade was opened/entered.
* Winning Days: Total number of winning trading days. A winning day is defined as a day with a positive total P&L for the overall day.
* Losing Days: Total number of losing trading days. A losing day is defined as a day with a negative total P&L for the overall day.
* Breakeven Days: Total number of trading days where you broke even, or closed the day with a $0 P&L
* Logged Day: A logged day is a day where you journaled for the day.
* Max Consecutive Winning Days: The number of consistent winning days in a row.
* Max Consecutive Losing Days: The number of consistent losing days in a row.
* Average Daily P&L: How much you typically make, on average, for the overall trading day.
* Average Winning Day P&L: How much you typically make, on average, on your winning trading days. A winning day is defined as a day where you closed with a positive total P&L.
* Average Losing Day P&L: How much you typically lose, on average, on your losing trading days. A losing day is defined as a day where you closed with a negative total P&L.
* Largest Profitable Day (Profits): Your largest profitable trading day.
* Largest Losing Day (Losses): Your largest total loss for the trading day.
* Average Planned R-Multiple: The average PLANNED R Multiple on all your trades. Note: This stat is based on trades where you manually inputted your ''profit target'' and ''stop loss'' for the trade.
* Average Realized R-Multiple: The average REALIZED R Multiple on all your trades. Note: This stat is based on trades where you manually inputted your ''stop loss'' for the trade. This measures your R multiple on all your trades and takes the average to display your average R multiple on your trades.
* Trade Expectancy: How much you are expected to make on your future trades, based on historical performance.
* Daily Net cumulative P&L: Displays how your total account P/L accumulated over the course of each trading
* NET DAILY P&L: Displays your total net profit/loss for each trading day on the day it is realized.
Some of the insightful information which traders can be extracted in general by maintaining such journal data
Trading Style Assessment
Average Hold Time (Winning vs Losing Trades)
- Longer hold on winning trades vs. shorter hold on losing trades = classic trend-follower style.
- Opposite pattern = scalper / mean reversion tendencies.
- Equal hold times → systematic execution, not dependent on trade outcome.
Average Daily Volume + Total Number of Trades
- High volume + high trade count → active intraday scalper.
- Low volume + fewer trades, longer holds → swing trader.
- Useful for checking if actual style matches intended style.
Key Performance Assessment
Profit Factor (PF) = Profits ÷ Losses
- PF > 1.5 → Edge confirmed.
- PF < 1.0 → Strategy needs fixing.
- PF between 1–1.3 → fragile edge; small mistakes can flip results.
Trade Expectancy
- If positive → every trade is statistically worth taking (good edge).
- If negative → each trade bleeds account even with discipline.
Average Realized R-Multiple
- Tracks whether you actually capture your planned RR or exit early.
- Consistently < Planned R → psychology issues (fear, impatience).
Max Consecutive Wins / Losses & Winning vs Losing Days
Shows streak resilience.
- If max losing streak is long but PF is still > 1.5 → robust system.
- If a few wins followed by a long string of losses → system too dependent on rare setups.
- If losing days are much larger in size than winning days → risk imbalance.
- Ideal profile = winning days ≥ losing days and more frequent.
- Smooth upward curve = consistent execution.
- Jagged up & down swings = inconsistent risk management.
Futuristic Predictions
Trade Expectancy + Win Rate + Average R
- These three combined forecast your equity curve growth. Example:
Expectancy = ₹500/trade , 20 trades/month → ₹10,000 expected monthly growth at current scale. Scaling factor: multiply expectancy by planned position size increases.
Consistency of Profit Factor & Drawdown Size
- Stable PF over months → system is robust across market regimes.
- High variance → system may break in certain volatility phases.
Maximum Consecutive Losing Days
- Useful for designing capital buffers. If max is 5, you know capital must withstand 5 losing days without psychological breakdown.
Average Realized R vs Planned R
- Consistently taking profits too early = fear of losing unrealized gains.
- Consistently holding losers longer than planned = hope bias.
- If largest loss > 2× largest profit → risk aversion missing; revenge/ego trades likely.
- Too many scratch trades → hesitation / lack of conviction.
- Almost none → either discipline is high OR you don’t cut early when setups fail.
- If journaling is consistent, drawdowns usually recover faster → psychological anchor.
- Gaps in logging often align with emotional turbulence.
Sizing & Scaling Insights
Position Sizing Efficiency
Metric: Average Losing Trade ÷ Account Capital
- Ideal: ≤1% per trade
- Warning: >2% = risk of ruin increases
- Tracker Insight → tells you if you’re oversizing relative to account
Metric: Largest Losing Day ÷ Weekly Average Profit
- Ideal: ≤1.5×
- If >2×, size per trade is too aggressive
Scaling Readiness Signals
Scaling should only happen when:
1. Profit Factor ≥ 1.5 for last 30–50 trades
2. Win Rate ≥ 55% OR Expectancy positive for 3 cycles in a row
3. Max Consecutive Losing Days ≤ 5 (so capital buffer is enough)
4. ROI Stability → ROI positive in ≥70% of cycles
This avoids scaling during “lucky streaks” and instead builds size on proven consistency.
Sizing Progression Rule (Compounding Plan)
- Fixed Risk % Model → Risk = 0.8–1% of account per trade
- Every time account grows by 10%, increase lot size proportionally
- If equity falls 10% below peak, scale down to protect capital
Metric: % of Total P&L coming from Top 3 trades
- If >50% → over-reliance on few oversized bets
- If <30% → healthy distribution, scaling is safer
- If emotions spike (hesitation, early exits, widening SLs), scale down and stabilize.
- Journaling entries should capture comfort level with bigger size.
Other Highly Useful Insights
Commissions/Fees/Swaps Impact
- If they eat >10% of gross profits → strategy relies on too many micro-trades → optimize style or execution costs.
- If “Largest Losing Day” wipes out more than 1 week of average profits → reduce per-trade risk.
- Compare Average Daily Volume, Trade Count, Hold Times month-to-month.
- If drifting, ask: Am I consciously adapting, or unconsciously deviating due to emotions?
1. Trading Style Mirror → Hold time + trade count will reveal if you’re truly a trend follower, scalper, or hybrid.
2. Edge Verification → Profit Factor + Trade Expectancy + Realized R are the “holy trinity” for proving if your system actually has positive expectancy.
3. Consistency Audit → Max streaks, daily P&L distribution, and cumulative curve show if you can scale confidently.
4. Psychological Fingerprint → Realized vs Planned R, scratch trades, and biggest win/loss asymmetry directly reflect discipline, fear, and greed patterns.
5. Scaling Blueprint → Expectancy × Volume = roadmap for growth. Once expectancy is consistently positive, scaling is purely a psychological and capital management challenge.