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Prerequisites: Technical Analysis Fundamentals

Overview

Item Description
Suitable for Readers who have not systematically studied candlesticks, moving averages, RSI, MACD, and other indicators
Estimated time 45–60 minutes
Already proficient? Continue from Tutorial 00: Environment & Quantitative Foundations or Tutorial 01: Writing Your First Strategy

This article is for readers who have never been exposed to technical analysis. If you already understand moving averages, RSI, and MACD, feel free to skip ahead to Tutorial 00: Environment & Quantitative Foundations.

Table of Contents

  1. Price Data: OHLCV
  2. Moving Averages (MA)
  3. RSI: Relative Strength Index
  4. MACD: Moving Average Convergence/Divergence
  5. Bollinger Bands
  6. ATR: Average True Range
  7. KDJ / Stochastic Oscillator
  8. ADX: Trend Strength
  9. Volume-Related Concepts
  10. Support & Resistance
  11. Corresponding Functions in eqlib.utils
  12. Next Steps

1. Price Data: OHLCV

Each candlestick (bar) contains 5 fields, collectively known as OHLCV:

Field Meaning
open Open price
high Highest price of the day
low Lowest price of the day
close Close price
volume Volume (number of shares traded)

Sometimes there is also money (turnover, in CNY) = price × volume (approximate).

# Fetching OHLCV data in eqlib
hist = attribute_history('601390', 20, '1d', ['open', 'high', 'low', 'close', 'volume'])
#           open   high    low  close      volume
# 2024-01-02  4.85   4.92   4.80   4.88  1234567890
# 2024-01-03  4.89   4.95   4.85   4.92  987654321

Why is the close price so commonly used? Most strategies use close because it represents the final market price of the day and is the most widely quoted figure. Some strategies also use high / low / volume (e.g., ATR requires all three, while Bollinger Bands only need the close price).

2. Moving Averages (MA)

2.1 Simple Moving Average (SMA)

The arithmetic mean of the most recent N close prices:

MA(N) = (close[0] + close[1] + ... + close[N-1]) / N

Typical parameters: MA5 (weekly), MA10 (bi-weekly), MA20 (monthly), MA60 (quarterly), MA120 (semi-annual), MA250 (annual)

from eqlib import *
from eqlib.utils import ma

hist = attribute_history('601390', 60, '1d', ['close'])
close = hist['close']

# Manual calculation
ma5  = close.tail(5).mean()
ma20 = close.tail(20).mean()

# Using eqlib.utils
ma5_series  = ma(close, 5)    # returns entire Series; use iloc[-1] for latest value
ma20_series = ma(close, 20)

2.2 Exponential Moving Average (EMA)

A moving average that assigns higher weight to recent data, reacting faster to price changes than SMA:

from eqlib.utils import ema

ema12 = ema(close, 12)
ema26 = ema(close, 26)

2.3 Wilder's Smoothing Moving Average (SMMA)

The smoothing method used internally by RSI, ATR, and ADX (also known as RMA / Wilder's Smoothing):

from eqlib.utils import smma

smma14 = smma(close, 14)

Note (project convention): eqlib.utils.sma() = simple rolling mean; eqlib.utils.smma() = Wilder's smoothing. Use smma() for RSI / ATR related indicators.

2.4 Common Uses of Moving Averages

Scenario Usage
Trend identification Price above MA = bullish; price below MA = bearish
Golden cross / Death cross Short MA crosses above long MA = buy signal (golden cross); crosses below = sell signal (death cross)
Support / Resistance The moving average itself often acts as a support or resistance level
Trend strength Multiple MAs in bullish alignment (short > medium > long) indicate a strong trend

3. RSI: Relative Strength Index

RSI (Relative Strength Index) measures the ratio of upward price momentum to downward price momentum, mapping the result to the 0–100 range.

3.1 Calculation Logic (Simplified)

1. Calculate the average of up-moves (up) and down-moves (down) over the past N days
2. RS = average up / average down
3. RSI = 100 - 100 / (1 + RS)

Typical parameter: period=14 (most commonly used)

3.2 Interpretation

RSI Value Meaning Strategy Implication
> 70 Overbought Price may fall, consider selling
50–70 Moderately bullish Bull market
30–50 Moderately bearish Bear market
< 30 Oversold Price may rebound, consider buying 
from eqlib.utils import rsi

hist = attribute_history('601390', 60, '1d', ['close'])
rsi14 = rsi(hist['close'], 14)   # returns Series
current_rsi = rsi14.iloc[-1]

if current_rsi < 30:
    print('Oversold, watch for buying opportunities')

4. MACD: Moving Average Convergence/Divergence

MACD (Moving Average Convergence/Divergence) derives three lines from the difference between two moving averages:

Line Calculation Meaning
DIF (MACD Line) EMA(12) − EMA(26) Difference between short-term and long-term MAs
DEA (Signal Line) EMA(DIF, 9) 9-day EMA of DIF (smoothed)
Histogram (DIF − DEA) × 2 Distance between the two lines; positive/negative = bullish/bearish

Interpretation

  • DIF crosses above DEA (histogram turns from negative to positive) → Buy signal ("golden cross")
  • DIF crosses below DEA (histogram turns from positive to negative) → Sell signal ("death cross")
  • Histogram transitions from shrinking to expanding (increasing momentum) → Trend strengthening
from eqlib.utils import macd

hist = attribute_history('601390', 60, '1d', ['close'])
dif, dea, histogram = macd(hist['close'], fast=12, slow=26, signal=9)

latest_dif = dif.iloc[-1]
latest_dea = dea.iloc[-1]

if latest_dif > latest_dea:
    print('MACD bullish alignment')

5. Bollinger Bands

Bollinger Bands consist of three lines based on the mean and standard deviation of the close price:

Line Calculation
Upper Band MA(N) + k × Std(N)
Middle Band MA(N) (typically N=20)
Lower Band MA(N) − k × Std(N)

Typical parameters: N=20, k=2 (i.e., ±2 standard deviations)

Interpretation

  • Price touches the upper band → Overbought, may pull back
  • Price touches the lower band → Oversold, may rebound
  • Band width narrows → Volatility decreasing, potentially building up for a big move
  • Price consistently rides the upper band → Strong trend, avoid shorting
from eqlib.utils import boll

hist = attribute_history('601390', 60, '1d', ['close'])
upper, middle, lower = boll(hist['close'], period=20, std_dev=2)

price = hist['close'].iloc[-1]

if price < lower.iloc[-1]:
    print('Price below lower Bollinger Band, mean-reversion buy signal')

6. ATR: Average True Range

ATR (Average True Range) measures the daily price volatility range, used for setting stop-losses and position sizing.

True Range (TR) Calculation

TR = max(High - Low, |High - PrevClose|, |Low - PrevClose|)
ATR(N) = Wilder's smoothed average(TR, N)

Typical parameter: period=14

Usage

from eqlib.utils import atr

hist = attribute_history('601390', 30, '1d', ['high', 'low', 'close'])
atr14 = atr(hist['high'], hist['low'], hist['close'], 14)
current_atr = atr14.iloc[-1]

# ATR stop-loss: current price minus 2x ATR
stop_loss_price = current_price - 2 * current_atr

# ATR position sizing: risk at most 1% of total capital per trade
risk_per_trade = total_value * 0.01
shares = risk_per_trade / (2 * current_atr)

7. KDJ / Stochastic Oscillator

KDJ (also known as the Stochastic Oscillator) consists of three lines that gauge overbought/oversold conditions based on the price's relative position within a range:

Line Meaning
K Fast stochastic value (more sensitive than D)
D Moving average of K (slower, acts as signal line)
J 3K − 2D (leading indicator, more extreme overbought/oversold readings)
  • J < 0 → Extremely oversold
  • J > 100 → Extremely overbought
  • K crosses above D → Buy signal; K crosses below D → Sell signal
from eqlib.utils import kdj

hist = attribute_history('601390', 30, '1d', ['high', 'low', 'close'])
k, d, j = kdj(hist['high'], hist['low'], hist['close'])

8. ADX: Trend Strength

ADX (Average Directional Index) measures trend strength without indicating direction:

ADX Value Trend Strength
< 20 No trend or weak trend (ranging market)
20–40 Moderate trend
> 40 Strong trend

ADX does not tell you whether the market is going up or down — only whether it is trending. It is commonly paired with moving averages: use trend-following strategies when ADX > 25, and switch to mean-reversion strategies when ADX < 20.

from eqlib.utils import adx

hist = attribute_history('601390', 30, '1d', ['high', 'low', 'close'])
adx_val, plus_di, minus_di = adx(hist['high'], hist['low'], hist['close'], period=14)
current_adx = adx_val.iloc[-1]
Concept Meaning In eqlib
Volume Number of shares traded today hist['volume']
Turnover (Amount/Money) Total value traded today hist['money']
Volume ratio Today's volume / average volume over past N days Manual calculation
OBV (On-Balance Volume) Cumulative: add volume on up-days, subtract on down-days eqlib.utils.obv()

Why volume matters: - Price rises on high volume → Strong bullish force, signal is more reliable - Price rises on low volume → Uptrend losing steam, exercise caution - Price falls on high volume → Strong bearish force - Price falls on low volume → Selling pressure may be fading, watch for rebound opportunities

# Volume ratio (today's volume / 20-day average volume)
hist = attribute_history('601390', 25, '1d', ['close', 'volume'])
avg_vol = hist['volume'].tail(20).mean()
today_vol = hist['volume'].iloc[-1]
volume_ratio = today_vol / avg_vol

if volume_ratio > 1.5:
    print('Above-average volume, signal is more reliable')

10. Support & Resistance

Support: A price level where the price repeatedly stops falling and bounces back up. Resistance: A price level where the price repeatedly stops rising and pulls back down.

Common sources of support/resistance: - Historical highs and lows - Round-number price levels (psychological levels, e.g., 5 CNY, 10 CNY, 100 CNY) - Moving averages (MA20, MA60, etc.) - Bollinger Band upper and lower bands - Fibonacci retracement levels (38.2%, 61.8%) - High-volume price zones (Volume Profile)

from eqlib.utils import support_resistance_levels, donchian

# Fibonacci retracement
from eqlib.utils import fibonacci_retracement

hist = attribute_history('601390', 60, '1d', ['high', 'low', 'close'])
high = hist['high'].max()
low  = hist['low'].min()
fib_levels = fibonacci_retracement(high, low)
# Returns prices at [0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%]

# Donchian Channel (N-day high/low)
upper_ch, lower_ch = donchian(hist['high'], hist['low'], period=20)

11. Corresponding Functions in eqlib.utils

Below is a mapping of the technical indicators discussed in this article to their eqlib.utils functions:

Indicator Function Returns
SMA (Simple Moving Average) utils.ma(series, period) Series
EMA (Exponential Moving Average) utils.ema(series, period) Series
Wilder's Smoothing utils.smma(series, period) Series
RSI utils.rsi(close, period=14) Series
MACD utils.macd(close, fast, slow, signal) (dif, dea, hist)
Bollinger Bands utils.boll(close, period=20, std_dev=2) (upper, mid, lower)
ATR utils.atr(high, low, close, period=14) Series
KDJ utils.kdj(high, low, close, ...) (k, d, j)
ADX utils.adx(high, low, close, period=14) (adx, +di, -di)
OBV utils.obv(close, volume) Series
Fibonacci Retracement utils.fibonacci_retracement(high, low) list of prices
Donchian Channel utils.donchian(high, low, period) (upper, lower)

For a complete function list and parameter reference, see the Utility Library Reference.

12. Next Steps