Learn TessTrade

Appearance

Reading the results ​

At the end of a backtest, TessTrade generates a results panel with several sections. Each one answers a different question about the strategy.

Panel overview ​

  1. Summary (quantitative metrics) - Sharpe, drawdown, profit factor, etc.
  2. Equity curve - capital chart over time.
  3. Trades - detailed list of each trade: entry, exit, P&L, duration.
  4. Distribution - histogram of gains/losses.
  5. Logs - script stdout (output from print()).

Equity curve ​

The equity line shows the evolution of total capital across the backtested period.

What to observe ​

  • General shape: rising indicates a profitable strategy; falling, a losing one; plateau, neutral.
  • Visible drawdowns: deep valleys represent periods of losses. Details in Performance metrics.
  • Consistency: a smooth curve with small oscillations is preferable to one with large sporadic jumps, even with similar total P&L.
  • Concentration period: if all performance comes from 2-3 trades on specific dates, the strategy is fragile.

Benchmark (optional) ​

If the UI offers a "buy and hold" overlay, compare strategy performance against simply buying and holding the asset. If the strategy does not outperform the benchmark, no additional value is added.

Trade list ​

Table detailing each round-trip (entry plus exit) with:

ColumnMeaning
Entry timeEntry timestamp (ms or date)
Entry pricePrice executed at entry
Exit timeExit timestamp
Exit pricePrice executed at exit
Sidelong or short
QtyPosition size
P&L (abs)Profit / loss in monetary units
P&L (%)Percentage return on allocated capital
DurationTime in position
FeesFees charged
SlippageExecution slippage

Useful filters ​

  • Sort by P&L: identifies the best and worst trades and exposes where the strategy wins or loses.
  • Filter by date: isolates specific periods, for example performance during a bear market versus a bull market.
  • Filter by side: separates long from short results.

Result distribution ​

Histogram of trades classified by P&L:

  • Many small losses and few large gains characterize typical trend-following.
  • Many small gains and few large losses characterize typical mean-reversion.
  • Anomalies: a single trade explaining 50% of the total P&L is a red flag and indicates luck rather than edge.

Logs ​

Capture of the script's stdout. Useful for debugging:

python
def on_bar_strategy(sdk, params):
    rsi = _rsi_last([c["close"] for c in sdk.candles], 14)
    print(f"[{sdk.candles[-1]['time']}] RSI={rsi:.2f} pos={sdk.position}")

Each print() generates a line in the log with a timestamp. Uncaught exceptions also appear here, with traceback.

Note: in scripts that run across thousands of candles, print() on every bar generates huge logs. Use sparingly or guard with if.

Sanity - quick checklist when opening results ​

Before considering a high Sharpe as definitive:

1. Is the number of trades reasonable? ​

  • Few trades (< 20): the result may be luck. Low statistical confidence.
  • Tens to hundreds: adequate base for conclusions.
  • Thousands in a short period: probable overreaction to noise. Review signals.

2. An extreme win rate is suspicious ​

  • > 80%: either the strategy is exceptional, or there is look-ahead bias zeroing unrealized losses and accounting only for gains.
  • < 40% but profitable: acceptable if expectancy (avg P&L x win_rate - avg loss P&L x loss_rate) is positive. Check profit factor.

3. Is the equity curve "too smooth"? ​

Very smooth strategies with no visible drawdown often indicate overfitting: parameters adjusted to fit historical data. Test on an out-of-sample period (another year) before trusting.

4. Performance concentrated on a few dates? ​

Sort trades by P&L and observe the top 5. If they sum to more than 30% of the total P&L, the strategy is fragile.

5. Max drawdown acceptable? ​

Drawdown is the largest valley of the equity curve. For most traders, above 30% is psychologically painful. If the backtest shows a 45% DD with a good Sharpe, evaluate tolerance before going live.

6. Are fees and slippage enabled? ​

Without fees and slippage, results are not realistic. TessTrade applies them by default; confirm in the backtest configuration section.

Settings relevant to results ​

Execution model ​

  • Pessimistic (default): in case of ambiguity (stop and target touched on the same candle), the stop wins. Conservative.
  • Optimistic: the target wins. Generates unrealistically optimistic results; use only for debug.

Slippage ​

In ticks. For crypto 1h, 2 is reasonable. For shorter intraday, increase.

Probability fill on limit ​

Controls how often a limit order fills when the price touches it. Lower values reflect realistic queue competition. Tune according to the market being simulated.

Fees ​

Maker/taker in bps. Configure according to the fee schedule of the venue being simulated.

Exporting results ​

The panel typically offers:

  • CSV of trades for analysis in Excel/Pandas.
  • JSON of equity curve to compare strategies externally.
  • PDF report as a printable summary.

Next steps ​