DEV LOG: PHASE 1
TREND BOT V1.01
THE STATIC RULE-BASED ERA
Welcome to Phase 1. This is Version 1.01. We are testing the raw performance of a classic EMA Crossover strategy with strict Money Management designed to catch massive moves.
Target Asset: BTCUSDm | Indicator: EMA 9 & 21 | TP: 300k / SL: 100k
UNIT: V1.01 (STATIC)
LIVE TESTING
FINAL P/L
IN PROGRESS
NET GROWTH
–%
WIN RATE
–%
TOTAL TRADES
—
CURRENT ACTIVE LOGIC: V1.01
ENTRY SIGNAL
Classic EMA Cross (9, 21) on H1 timeframe.
BUY when EMA9 crosses above EMA21 | SELL when EMA9 crosses below.
BUY when EMA9 crosses above EMA21 | SELL when EMA9 crosses below.
EXIT STRATEGY
Take Profit 300,000 Points & Stop Loss 100,000 Points. Risk/Reward is exactly 1:3.
POSITION SIZING
Fixed Volume 0.01 Lot. Single order execution only.
TRAILING STOP
DISABLED.
Fixed static target used. No dynamic trailing logic in Phase 1 baseline testing.
Fixed static target used. No dynamic trailing logic in Phase 1 baseline testing.
DEV LOG: WEEK 1 (V1.01)
V1.01: CLASSIC EMA CROSSOVER
To catch a massive trend, we start with the most famous indicator setup in algorithmic trading: a fast EMA crossing a slower EMA. The goal is to evaluate if this simple logic can generate a sustainable edge when paired with high Risk/Reward.
V1.01 Hypothesis & Expectations
We are deploying the bot with standard EMA 9/21 cross and fixed 300,000 points TP / 100,000 points SL on the H1 timeframe.
- The Hypothesis: By using a 1:3 R:R on the H1 timeframe, we aim to filter out intraday chop and catch massive macro trends. With a 1:3 ratio, the system only needs a Win Rate of ~26% to remain profitable.
- Potential Risk: Moving averages are lagging indicators. In a tight, ranging market, the lines will cross back and forth repeatedly, causing false signals and multiple consecutive stop-outs before a real trend emerges.
core_logic_v101.py
def check_ema_cross(df):
# Calculate fast and slow EMAs
df['EMA_9'] = ta.trend.ema_indicator(df['close'], window=9)
df['EMA_21'] = ta.trend.ema_indicator(df['close'], window=21)
ema9_prev, ema9_curr = df['EMA_9'].iloc[-3], df['EMA_9'].iloc[-2]
ema21_prev, ema21_curr = df['EMA_21'].iloc[-3], df['EMA_21'].iloc[-2]
if ema9_prev <= ema21_prev and ema9_curr > ema21_curr:
return "BUY"
elif ema9_prev >= ema21_prev and ema9_curr < ema21_curr:
return "SELL"
RAW EXECUTION LOGS
FETCHING V1.01 RECORDS…
ABOUT VERSION 1.01
What is the strategy for V1.01?
Version 1.01 is the most basic iteration. It uses a static rule set (EMA Cross 9/21) but incorporates advanced Money Management from day one by demanding a 1:3 Risk/Reward ratio on the H1 timeframe.
Why force a 1:3 Risk/Reward?
Trend following strategies naturally have lower win rates due to false breakouts (whipsaws) in ranging markets. A 1:3 ratio ensures that when we finally catch the real trend, that single large win easily covers multiple previous small losses.
Why is there no trailing stop?
This is our baseline test. Before exploring dynamic trailing techniques, we need to document the true success rate of a fixed, static target in trending conditions.
Educational & Research Purposes Only (Dev Log):
This archive displays raw data from an algorithmic trading experiment. It is NOT a solicitation to invest, financial advice, or a guarantee of future profits. Trading involves high risk. Past performance is not indicative of future results.