u/AdventurousFlow8993

[deleted]

My bot’s “Event Radar” detected this FOMO coin and added it to the watchlist.

The radar doesn’t open trades by itself.
It simply highlights where the market activity is.

The strategy runs independently based on its own rules.
In this case, the entry was triggered by the strong red candle and selling pressure on the left.

The first attempt was stopped out,
as price made a small upward reaction.

After reviewing the trade, I realized the issue was the stop distance.
The wick was unusually large, which pushed the stop too far from entry.

With a fixed risk model, this can create unnecessary exposure
and even increase liquidation risk before the stop is reached.

I’m now adjusting the logic:
if the wick is too large, the stop will be tightened,
or the trade will be skipped entirely.

So basically:
Radar finds the coin,
Strategy picks the timing,
Risk management keeps it alive.

I also receive radar alerts as notifications,
which allows me to monitor the process manually as well.

Today I noticed something interesting while running my bot live.

When price hit my stop level, the bot was closing the position,
but on Binance the trade wasn’t actually closed.

In short:

• the bot thinks the position is closed
• the exchange keeps it open

This kind of issue is hard to catch,
but after making the system more modular, it became much easier to isolate and fix.

So I changed the logic:

Instead of triggering stops based on price,
I now rely on the actual position state on the exchange.

If the position size is zero → it’s really closed
If not → do nothing

Now the system behaves much more reliably in live trading.

**Same strategy — different results depending on time period**

| BACKTEST 1 | BACKTEST 2 | BACKTEST 3 |

|------------|------------|------------|------

| Coins | 50 | 15 | 50

| Trades | 96 | 20 | 40

| Winrate | 32.6% | 45% | 43.6% |

| Net | +13.5R | +11.5R | +30.75R |

| Max DD | -16.7% | -2.8% | -4.9% |

| L Streak | 12 | 2 | 4 |

----------------------------------------------------------------

Note: Backtest 3 uses RSI 70. Others use the same setup.

**SYSTEM SETUP**

RISK MODEL

- Fixed risk (~1R)

- Stop: above wick

- TP: 2.5R (full)

- No scaling / no averaging

- One position per coin

***********************************

STRATEGY

- EMA

- RSI

- Bollinger

- Wick

- Entry: candle close

--------------------------

I’ve been working on a short-focused trading bot and testing the same strategy across different backtests.

Nothing changes — same rules, same structure.

But when I change the time period, the results vary significantly.

The system clearly performs better during weaker market conditions and downtrends.

So my main question is:

**Is performance driven by the strategy itself, or by selecting the right time period?**

***********************************************************************************************

*Backtests are simulations and do not guarantee real trading performance..

for a long time i kept adding indicators trying to improve my system

but no matter how many filters i added the results didn’t improve

so i changed my approach

instead of using indicators as entry signals

i now use them only as context

the actual entry logic is simple

clear rejection candles near local highs

no prediction

no guessing

just reaction to price

rules are simple

- price must be extended

- RSI above 60 (not a signal, just context)

- strong upper wick

- close near the low

recent backtest results

- total trades: 128

- win rate: 35.1%

- model: 2.5R TP / 1R SL

- avg trade: +0.23R

- timeframe: 15m

- period: Nov 10 – Nov 18

win rate is not high

but the risk/reward structure keeps the system profitable

currently working on improving exits

and avoiding unnecessary trades during strong trend conditions

in the screenshot the system is actively scanning

but not taking trades because there is no valid signal

it doesn’t trade every move

only when conditions are met

curious if anyone else moved away from indicator-heavy systems

to simpler price action approaches

especially interested in how you filter strong trend conditions